论文检索

Theory · Domain Adaptation and Transfer Learning

Ziyi Wang, Zhi Gao, Boxuan Yu, Zirui Dai, Peiyao Wang, Yuxiang Song, Qingyuan Lu, Jin Chen, Xinxiao Wu

Video understanding has made remarkable progress in recent years, largely driven by advances in deep models and the availability of large-scale annotated datasets. However, the robustness of these models to domain shifts encountered in real-world video applications remains a critical yet underexplored problem, limiting their practical reliability. To address this problem, we introduce \textbf{V}ideo \textbf{U}nderstanding \textbf{D}omain \textbf{G}eneralization (\textbf{VUDG}), the first dataset designed specifically for evaluating domain generalization in video understanding. VUDG contains videos from 11 distinct domains that cover three types of domain shifts, and maintains semantic consistency across different domains to ensure fair and meaningful evaluation. We propose a multi-expert progressive annotation framework to efficiently annotate videos with structured question-answer pairs designed for domain generalization. Extensive experiments on 9 representative Large Vision-Language Models (LVLMs) and several traditional video question answering methods show that most models (including state-of-the-art LVLMs) suffer performance degradation under domain shifts. These results highlight the challenges posed by VUDG and the difference in the robustness of current models to data distribution shifts. We believe VUDG provides a critical resource to benefit future research in domain generalization for video understanding.

Computer Vision · Vision Models & Multimodal

Zongshang Pang, Mayu Otani, Yuta Nakashima

Temporally localizing user-queried events through natural language is a crucial capability for video models. Recent methods predominantly adapt video LLMs to generate event boundary timestamps for temporal localization tasks, which struggle to leverage LLMs' pre-trained semantic understanding capabilities due to the uninformative nature of timestamp outputs. In this work, we explore a timestamp-free, semantic-oriented framework that fine-tunes video LLMs using two generative learning tasks and one discriminative learning task. We first introduce a structural token generation task that enables the video LLM to recognize the temporal structure of input videos based on the input query. Through this task, the video LLM generates a sequence of special tokens, called structural tokens, which partition the video into consecutive segments and categorize them as either target events or background transitions. To enhance precise recognition of event segments, we further propose a query-focused captioning task that enables the video LLM to extract fine-grained event semantics that can be effectively utilized by the structural tokens. Finally, we introduce a structural token grounding module driven by contrastive learning to associate each structural token with its corresponding video segment, achieving holistic temporal segmentation of the input video and readily yielding the target event segments for localization. Extensive experiments across diverse temporal localization tasks demonstrate that our proposed framework, MeCo, consistently outperforms methods relying on boundary timestamp generation, highlighting the potential of a semantic-driven approach for temporal localization with video LLMs.

Computer Vision · Vision Models & Multimodal

Xinyi Xu, Hongsong Wang, Guo-Sen Xie, Caifeng Shan, Fang Zhao

Temporal grounding in long, untrimmed videos is critical for real-world video understanding, yet it remains a challenging task owing to complex temporal structures and pervasive visual redundancy. Existing methods rely heavily on supervised training with task-specific annotations, which inherently limits their scalability and adaptability due to the substantial cost of data collection and model retraining. Although a few recent works have explored training-free or zero-shot grounding, they seldom address the unique challenges posed by long videos. In this paper, we propose HiTeA (Hierarchical Temporal Alignment), a novel, training-free framework explicitly designed for long-video temporal grounding. HiTeA introduces a hierarchical temporal decomposition mechanism that structures videos into events, scenes, and actions, thereby aligning natural language queries with the most appropriate temporal granularity. Candidate segments are then matched with queries by leveraging pre-trained vision–language models (VLMs) to directly compute segment–text similarity, thereby obviating the need for any task-specific training or fine-tuning. Extensive experiments on both short- and long-video benchmarks show that HiTeA not only substantially outperforms all existing training-free methods (e.g., achieving 44.94% R\@0.1 on TACoS, representing an absolute gain of 12.4%) but also achieves competitive performance against state-of-the-art supervised baselines under stricter metrics. The code is available at https://anonymous.4open.science/r/HiTeA_code.

Computer Vision · Vision Models & Multimodal

Xinlong Chen, Yue Ding, Weihong Lin, Jingyun Hua, Linli Yao, Yang Shi, Bozhou Li, Qiang Liu, Yuanxing Zhang, Pengfei Wan 等

Audiovisual video captioning aims to generate semantically rich descriptions with temporal alignment between visual and auditory events, thereby benefiting both video understanding and generation. In this paper, we present **AVoCaDO**, a powerful audiovisual video captioner driven by the temporal orchestration between audio and visual modalities. We propose a two-stage post-training pipeline: (1) **AVoCaDO SFT**, which fine-tunes the model on a newly curated dataset of 107K high-quality, temporally-aligned audiovisual captions; and (2) **AVoCaDO GRPO**, which leverages tailored reward functions to further enhance temporal coherence and dialogue accuracy while regularizing caption length and reducing collapse. Experimental results demonstrate that AVoCaDO significantly outperforms existing open-source models across four audiovisual video captioning benchmarks, and also achieves competitive performance on the VDC benchmark under visual-only settings. The model will be made publicly available to facilitate future research in audiovisual video understanding and generation.

Computer Vision · 3D Rendering & Reconstruction

Feiran Wang, Junyi Wu, Dawen Cai, Yuan Hong, Yan Yan

We present CogniMap3D, a bioinspired framework for dynamic 3D scene understanding and reconstruction that emulates human cognitive processes. Our approach maintains a persistent memory bank of static scenes, enabling efficient spatial knowledge storage and rapid retrieval. CogniMap3D integrates three core capabilities: a multi-stage motion cue framework for identifying dynamic objects, a cognitive mapping system for storing, recalling, and updating static scenes across multiple visits, and a factor graph optimization strategy for refining camera poses. Given an image stream, our model identifies dynamic regions through motion cues with depth and camera pose priors, then matches static elements against its memory bank. When revisiting familiar locations, CogniMap3D retrieves stored scenes, relocates cameras, and updates memory with new observations. Evaluations on video depth estimation, camera pose reconstruction, and 3D mapping tasks demonstrate its state-of-the-art performance, while effectively supporting continuous scene understanding across extended sequences and multiple visits.

Computer Vision · Image and Video Generation

taihang Hu, Mengting Chen, Jinsong Lan, Xiaoyong Zhu, Kaifu Zhang, Ming-Ming Cheng, Bo Zheng, Yaxing Wang

Unified multimodal Large Language Models (MLLMs) hold great promise for seamlessly integrating understanding and generation. However, monolithic autoregressive architectures, despite their elegance and conversational fluency, suffer from a fundamental semantic–structural conflict: optimizing for low-level reconstructability in generation leads to catastrophic forgetting of high-level semantic understanding. We present ORION, a unified framework that resolves this conflict through Decoupling and Alignment. A non-linear vision head decouples structural pressures from shared representations, while a novel Representation Consistency Loss explicitly aligns semantics during generation. Together with a curated progressive training recipe and high-quality multimodal data, our method enables balanced optimization of both capabilities. Built purely on a monolithic autoregressive backbone without task-specific separate parameters, ORION achieves performance on par with or exceeding recent state-of-the-art unified models that rely on more complex designs. These results validate monolithic autoregression as a simple, effective, and competitive path toward truly integrated multimodal intelligence.

Computer Vision · Image and Video Generation

Hao Chen, Rui Yin, Yifan Chen, Qi Chen, Chao Li

Understanding disease progression is a central clinical challenge with direct implications for early diagnosis and personalized treatment. While recent generative approaches have attempted to model progression, key mismatches remain: disease dynamics are inherently continuous and monotonic, yet latent representations are often scattered, lacking semantic structure, and diffusion-based models disrupt continuity through the random denoising process. In this work, we propose treating disease dynamics as a velocity field and leveraging Flow Matching (FM) to align the temporal evolution of patient data. Unlike prior methods, our approach captures the intrinsic dynamics of disease, making progression more interpretable. However, a key challenge remains: in latent space, Autoencoders (AEs) do not guarantee alignment across patients or correlation with clinical severity (e.g., age and disease conditions). To address this, we propose learning patient-specific latent alignment, which enforces patient trajectories to lie along a specific axis, with magnitudes increasing monotonically with disease severity. This leads to a consistent and semantically meaningful latent space. Together, we present ∆-LFM, a framework for modeling patient-specific latent progression with flow matching. Across three longitudinal MRI benchmarks, ∆-LFM demonstrates strong empirical performance and, more importantly, establishes a new framework for interpreting and visualizing disease dynamics.

Computer Vision · Image and Video Generation

Minglei Shi, Haolin Wang, Wenzhao Zheng, Ziyang Yuan, Xiaoshi Wu, Xintao WANG, Pengfei Wan, Jie Zhou, Jiwen Lu

Recent progress in diffusion-based visual generation has largely relied on latent diffusion models with Variational Autoencoders (VAEs). While effective for high-fidelity synthesis, this VAE+Diffusion paradigm still suffers from limited training and inference efficiency, along with poor transferability to broader vision tasks. These issues stem from a key limitation of VAE latent spaces: the lack of clear semantic separation and strong discriminative structure. Our analysis confirms that these properties are not only crucial for perception and understanding tasks, but also equally essential for the stable and efficient training of latent diffusion models. Motivated by this insight, we introduce **SVG**—a novel latent diffusion model without variational autoencoders, which unleashes **S**elf-supervised representations for **V**isual **G**eneration. SVG constructs a feature space with clear semantic discriminability by leveraging frozen DINO features, while a lightweight residual branch captures fine-grained details for high-fidelity reconstruction. Diffusion models are trained directly on this semantically structured latent space to facilitate more efficient learning. As a result, SVG enables accelerated diffusion training, supports few-step sampling, and improves generative quality. Experimental results further show that SVG preserves the semantic and discriminative capabilities of the underlying self-supervised representations, providing a principled pathway toward task-general, high-quality visual representations.

Xize Cheng, Chenyuhao Wen, Slytherin Wang, Yongqi Wang, zehan wang, Rongjie Huang, Tao Jin, Zhou Zhao

Video Query Sound Separation (VQSS) aims to isolate target sounds conditioned on visual queries while suppressing off-screen interference—a task central to audiovisual understanding. However, existing methods often fail under conditions of homogeneous interference and overlapping soundtracks, due to limited temporal modeling and weak audiovisual alignment. We propose \textbf{AlignSep}, the first generative VQSS model based on flow matching, designed to address common issues such as spectral holes and incomplete separation. To better capture cross-modal correspondence, we introduce a series of temporal consistency mechanisms that guide the vector field estimator toward learning robust audiovisual alignment, enabling accurate and resilient separation in complex scenes. As a \textit{multi-conditioned generation} task, VQSS presents unique challenges that differ fundamentally from traditional flow matching setups. We provide an in-depth analysis of these differences and their implications for generative modeling. To systematically evaluate performance under realistic and difficult conditions, we further construct \textbf{VGGSound-Hard}, a challenging benchmark composed entirely of separation cases with homogeneous interference and strong reliance on temporal visual cues. Extensive experiments across multiple benchmarks demonstrate that AlignSep achieves state-of-the-art performance both quantitatively and perceptually, validating its practical value for real-world applications. More results and audio examples are available at: \url{https://AlignSep.github.io}.

Ziyang Fan, Keyu Chen, Ruilong Xing, Yulin Li, Li Jiang, Zhuotao Tian

Although Video Large Language Models (VLLMs) have shown remarkable capabilities in video understanding, they are required to process high volumes of visual tokens, causing significant computational inefficiency. Existing VLLMs acceleration frameworks usually compress spatial and temporal redundancy independently, which overlooks the spatiotemporal relationships, thereby leading to suboptimal spatiotemporal compression. The highly correlated visual features are likely to change in spatial position, scale, orientation, and other attributes over time due to the dynamic nature of video. Building on this insight, we introduce FlashVID, a training-free inference acceleration framework for VLLMs. Specifically, FlashVID utilizes Attention and Diversity-based Token Selection (ADTS) to select the most representative tokens for basic video representation, then applies Tree-based Spatiotemporal Token Merging (TSTM) for fine-grained spatiotemporal redundancy elimination. Extensive experiments conducted on three representative VLLMs across five video understanding benchmarks demonstrate the effectiveness and generalization of our method. Notably, by retaining only $\textbf{10}$% of visual tokens, FlashVID preserves $\textbf{99.1}$% of the performance of LLaVA-OneVision. Consequently, FlashVID can serve as a training-free and plug-and-play module for extending long video frames, which enables a $\textbf{10$\times$}$ increase in video frame input to Qwen2.5-VL, resulting in a relative improvement of $\textbf{8.6}$% within the same computational budget. Code is available at https://github.com/Fanziyang-v/FlashVID.

Yifan Xu, Xinhao Li, Yichun Yang, Desen Meng, Rui Huang, Limin Wang

Video understanding, including video captioning and retrieval, is still a great challenge for video-language models (VLMs). The existing video retrieval and caption benchmarks only include short descriptions, limits their ability of detailed video understanding evaluation. To address this problem, we present CaReBench, a testing benchmark for fine-grained video Captioning and Retrieval with 1,000 high-quality pairs of videos and human-annotated detailed captions. Uniquely, it provides manually separated spatial annotations and temporal annotations for each video. Based on this design, we introduce two evaluation metrics, ReBias and CapST, specifically tailored for video retrieval and video captioning tasks, respectively. These metrics enable a comprehensive investigation into the spatial and temporal biases inherent in VLMs. In addition, to handle both video retrieval and video captioning tasks in a unified framework, we develop a simple baseline based on a Multimodal Language Model (MLLM). By implementing a two-stage Supervised Fine-Tuning (SFT), we fully unlock the potential of MLLM, enabling it not only to generate detailed video descriptions but also to extract video features. Surprisingly, experimental results demonstrate that, compared to the CLIP-based models designed for retrieval and the popular MLLMs skilled in video captioning, our baseline shows competitive performance in both fine-grained video retrieval and video detailed captioning.

Computer Vision · Classification and Understanding

Zhaolin Cai, Fan Li, Huiyu Duan, Lijun He, Guangtao Zhai

Video anomaly detection (VAD) aims to identify abnormal events in videos. Traditional VAD methods generally suffer from the high costs of labeled data and full training, thus some recent works have explored leveraging frozen multi-modal large language models (MLLMs) in a tuning-free manner to perform VAD. However, their performance is limited as they directly inherit pre-training biases and cannot adapt internal representations to specific video contexts, leading to difficulties in handling subtle or ambiguous anomalies. To address these limitations, we propose a novel intervention framework, termed SteerVAD, which advances MLLM-based VAD by shifting from passively reading to actively steering and rectifying internal representations. Our approach first leverages the gradient-free representational separability analysis (RSA) to identify top attention heads as latent anomaly experts (LAEs) which are most discriminative for VAD. Then a hierarchical meta-controller (HMC) generates dynamic rectification signals by jointly conditioning on global context and these LAE outputs. The signals execute targeted, anisotropic scaling directly upon the LAE representation manifolds, amplifying anomaly-relevant dimensions while suppressing inherent biases. Extensive experiments on mainstream benchmarks demonstrate our method achieves state-of-the-art performance among tuning-free approaches requiring only 1\% of training data, establishing it as a powerful new direction for video anomaly detection. The code will be released upon the publication.

Computer Vision · Vision Models & Multimodal

Junwen Pan, Qizhe Zhang, Rui Zhang, Ming Lu, Xin Wan, Yuan Zhang, Chang Liu, Qi She

Temporal search aims to identify a minimal set of relevant frames from tens of thousands based on a given query, serving as a foundation for accurate long-form video understanding. Many existing works attempt to progressively narrow the search space. However, these approaches typically rely on a hand-crafted search process, lacking end-to-end optimization for learning optimal search strategies. In this paper, we propose **TimeSearch-R**, which reformulates temporal search as interleaved text–video thinking, seamlessly integrating searching video clips into the reasoning process through reinforcement learning (RL). However, applying RL training methods, such as Group Relative Policy Optimization (GRPO), to video reasoning can result in unsupervised intermediate search decisions. This leads to insufficient exploration of the video content and inconsistent logical reasoning. To address these issues, we introduce GRPO with Completeness Self-Verification (GRPO-CSV), which gathers searched video frames from the interleaved reasoning process and utilizes the same policy model to verify the adequacy of searched frames, thereby improving the completeness of video reasoning. Additionally, we construct datasets specifically designed for the SFT cold-start and RL training of GRPO-CSV, filtering out samples with weak temporal dependencies to enhance task difficulty and improve temporal search capabilities. Extensive experiments demonstrate that TimeSearch-R achieves substantial improvements on temporal search benchmarks such as Haystack-LVBench and Haystack-Ego4D, long-form video understanding benchmarks like VideoMME, MLVU, and LongVideoBench, as well as video reasoning benchmarks such as Video-Holmes, consistently and significantly outperforming other existing temporal search approaches and text-only reasoning models. Our code is available at *https://github.com/Time-Search/TimeSearch-R*.

Computer Vision · Vision Models & Multimodal

Bo Fang, YuXin Song, Haoyuan Sun, Qiangqiang Wu, Wenhao Wu, Antoni Chan

Employing Multimodal Large Language Models (MLLMs) for long video understanding remains a challenging problem due to the dilemma between the substantial number of video frames (i.e., visual tokens) versus the limited context length of language models. Traditional uniform sampling often leads to selection of irrelevant content, while post-training MLLMs on thousands of frames imposes a substantial computational burden. In this paper, we propose _Narrating KeyFrames Capturing_ (Nar-KFC), a plug-and-play module to facilitate effective and efficient long video perception. Nar-KFC generally involves two collaborative steps. First, we formulate the _keyframe_ selection process as an integer quadratic programming problem, jointly optimizing query-relevance and frame-diversity. To avoid its computational complexity, a customized greedy search strategy is designed as an efficient alternative. Second, to mitigate the temporal discontinuity caused by sparse keyframe sampling, we further introduce interleaved textual _narratives_ generated from non-keyframes using off-the-shelf captioners. These narratives are inserted between keyframes based on their true temporal order, forming a coherent and compact representation. Nar-KFC thus serves as a temporal- and content-aware compression strategy that complements visual and textual modalities. Experimental results on multiple long-video benchmarks demonstrate that Nar-KFC significantly improves the performance of popular MLLMs. Codes are publicly available at https://github.com/bofang98/Nar-KFC.

Computer Vision · Vision Models & Multimodal

Yueqian Wang, Songxiang Liu, Disong Wang, Nuo Xu, Guanglu Wan, Huishuai Zhang, Dongyan Zhao

Recent advances in video multimodal large language models (Video MLLMs) have significantly enhanced video understanding and multi-modal interaction capabilities. While most existing systems operate in a turn-based manner where the model can only reply after user turns, proactively deciding when to reply during video playback presents a promising yet challenging direction for real-time applications. In this work, we propose a novel text-to-text approach to proactive interaction, where the model autonomously determines whether to respond or remain silent at each turn based on dialogue history and visual context up to current frame of an streaming video. To overcome difficulties in previous methods such as manually tuning response decision thresholds and annotating precise reply times, we introduce a multi-turn RL based training method that encourages timely and accurate responses without requiring precise response time annotations. We train our model MMDuet2 on a dataset of 52k videos with two types of dialogues via SFT and RL. Experimental results demonstrate that MMDuet2 outperforms existing proactive Video MLLM baselines in response timing and quality, achieving state-of-the-art performance on the ProactiveVideoQA benchmark.

Computer Vision · Vision Models & Multimodal

Kaichen Zhou, Yuhan Wang, Grace Chen, Gaspard Beaudouin, Fangneng Zhan, Paul Liang, Mengyu Wang

Recent 3D feed-forward models, such as the Visual Geometry Grounded Transformer (VGGT), have shown strong capability in inferring 3D attributes of static scenes. However, since they are typically trained on static datasets, these models often struggle in real-world scenarios involving complex dynamic elements, such as moving humans or deformable objects like umbrellas. To address this limitation, we introduce PAGE-4D, a feedforward model that extends VGGT to dynamic scenes, enabling camera pose estimation, depth prediction, point cloud reconstruction, and point tracking—all without post-processing. Training a geometry transformer for dynamic scenes from scratch, however, demands large-scale dynamic datasets and substantial computational resources, which are often impractical. To overcome this, we propose an efficient fine-tuning strategy that allows PAGE-4D to generalize to dynamic scenarios using only limited dynamic data and compute. In particular, we design a dynamics-aware aggregator that disentangles dynamic from static content for downstream scene understanding tasks: it first predicts a dynamics-aware mask, which then guides a dynamics-aware global attention mechanism. Extensive experiments show that PAGE-4D consistently outperforms the original VGGT in dynamic scenarios, achieving superior results in camera pose estimation, monocular and video depth estimation, and dense point map reconstruction. The source code and pretrained model weights are provided in the https://page4d.github.io.

Applications · Neuroscience, Cognitive Science

Yanchen Wang, Joy Hsu, Ehsan Adeli, Jiajun Wu

We propose NEURONA, a neuro-symbolic framework for fMRI decoding and concept grounding in neural activity. Leveraging image- and video-based fMRI question-answering datasets, NEURONA learns to decode interacting concepts from visual stimuli based on patterns of fMRI responses, integrating symbolic reasoning and compositional execution with fMRI grounding across brain regions. We demonstrate that incorporating structural priors (e.g., compositional predicate-argument dependencies between concepts) into the decoding process significantly improves both decoding accuracy over precise queries, and notably, generalization to unseen queries at test time. With NEURONA, we highlight neuro-symbolic frameworks as promising tools for understanding neural activity.

Computer Vision · Vision Models & Multimodal

Yepeng Tang, Weining Wang, Longteng Guo, Tongtian Yue, Wenxuan Wang, Chunjie Zhang, Jing Liu

Recent advances in Video LLMs have improved video understanding performance, but temporally grounded understanding in long-form videos remains challenging. Most models encode video frames into a flat sequence of visual tokens, which are then processed together with textual input by the LLM. While effective for short videos, this approach becomes inefficient for long-form videos due to lengthy token sequences that exceed context limits and incur high computational costs. Slow-Fast architectures partially address this by separating temporal and spatial features during encoding, but these features are still processed jointly within the LLM, lacking true spatio-temporal disentanglement. Moreover, spatial features are typically sampled in a query-agnostic manner, risking the loss of task-relevant content. To address these limitations, we propose Divid, a novel dual-branch framework that explicitly disentangles spatial and temporal modeling within the LLM decoder. Specifically, the temporal branch processes densely sampled, low-resolution frames to effectively capture long-range motion dynamics, while the spatial branch selects a sparse set of high-resolution keyframes guided by temporal attention. To unify the two branches, we design a lightweight spatio-temporal soft-router that adaptively fuses temporal and spatial cues at the token level, conditioned on the input query. This disentangled architecture not only improves temporal alignment accuracy but also leads to computational savings by minimizing redundant visual processing. Furthermore, we introduce TempGCap, a large-scale dataset consisting of 559K timestamp-grounded video-text pairs, providing rich temporal supervision. Extensive experiments on temporal grounding and grounded videoQA benchmarks demonstrate the superior performance and efficiency of our proposed Divid.

Computer Vision · Image and Video Generation

Kang Liao, Size Wu, Zhonghua Wu, Linyi Jin, Chao Wang, Yikai Wang, Fei Wang, Wei Li, Chen Change Loy

Camera-centric understanding and generation are two cornerstones of spatial intelligence, yet they are typically studied in isolation. We present Puffin, a unified camera-centric multimodal model that extends spatial awareness along the camera dimension. Puffin integrates language regression and diffusion-based generation to interpret and create scenes from arbitrary viewpoints. To bridge the modality gap between cameras and vision-language, we introduce a novel paradigm that treats camera as language, enabling thinking with camera. This guides the model to align spatially grounded visual cues with photographic terminology while reasoning across geometric context. Puffin is trained on Puffin-4M, a large-scale dataset of 4 million vision-language-camera triplets. We incorporate both global camera parameters and pixel-wise camera maps, yielding flexible and reliable spatial generation. Experiments demonstrate Puffin’s superior performance over specialized models for camera-centric generation and understanding. With instruction tuning, Puffin generalizes to diverse cross-view tasks such as spatial imagination, world exploration, and photography guidance. We will release the code, models, dataset pipeline, and benchmark to advance multimodal spatial intelligence research.

Computer Vision · Image and Video Generation

Haoran Bai, Xiaoxu Chen, Canqian Yang, Zongyao He, Sibin Deng, Ying Chen

We present Vivid-VR, a DiT-based generative video restoration method built upon an advanced T2V foundation model, where ControlNet is leveraged to control the generation process, ensuring content consistency. However, conventional fine-tuning of such controllable pipelines frequently suffers from distribution drift due to limitations in imperfect multimodal alignment, resulting in compromised texture realism and temporal coherence. To tackle this challenge, we propose a concept distillation training strategy that utilizes the pretrained T2V model to synthesize training samples with embedded textual concepts, thereby distilling its conceptual understanding to preserve texture and temporal quality. To enhance generation controllability, we redesign the control architecture with two key components: 1) a control feature projector that filters degradation artifacts from input video latents to minimize their propagation through the generation pipeline, and 2) a new ControlNet connector employing a dual-branch design. This connector synergistically combines MLP-based feature mapping with cross-attention mechanism for dynamic control feature retrieval, enabling both content preservation and adaptive control signal modulation. Extensive experiments show that Vivid-VR performs favorably against existing approaches on both synthetic and real-world benchmarks, as well as AIGC videos, achieving impressive texture realism, visual vividness, and temporal consistency. The codes and checkpoints are publicly available at https://github.com/csbhr/Vivid-VR.