论文检索

Minghan Li, Chenxi Xie, Yichen Wu, Lei Zhang, Mengyu Wang

Numerous text-to-video (T2V) editing methods have emerged recently, but the lack of a standardized benchmark for fair evaluation has led to inconsistent claims and an inability to assess model sensitivity to hyperparameters. Fine-grained video editing is crucial for enabling precise, object-level modifications while maintaining context and temporal consistency. To address this, we introduce FiVE-Bench, a Fine-grained Video Editing Benchmark for evaluating emerging diffusion and rectified flow models. Our benchmark includes 74 real-world videos and 26 generated videos, featuring 6 fine-grained editing types, 420 object-level editing prompt pairs, and their corresponding masks. Additionally, we adapt the latest rectified flow (RF) T2V generation models--Pyramid-Flow [??] and Wan2.1 [??]--by introducing FlowEdit [??], resulting in training-free and inversion-free video editing models Pyramid-Edit and Wan-Edit. We compare five diffusion methods with our two RF methods on the proposed FiVE-Bench, evaluating them across 15 metrics. These metrics include background preservation, text-video similarity, temporal consistency, and generated video quality. To further enhance object-level evaluation, we introduce FiVE-Acc, a novel metric leveraging Vision-Language Models (VLMs) to assess the success of fine-grained video editing. Experimental results demonstrate that RF-based editing significantly outperforms diffusion-based methods, with Wan-Edit achieving the best overall performance and exhibiting the least sensitivity to hyperparameters. More video demo available on the website: https://sites.google.com/view/five-benchmark.

Jiawei Mao, Yuhan Wang, Yucheng Tang, Daguang Xu, Kang Wang, Yang Yang, Zongwei Zhou, Yuyin Zhou

This paper presents **MedSegFactory**, a versatile medical synthesis framework that generates high-quality paired medical images and segmentation masks across modalities and tasks. It aims to serve as an unlimited data repository, supplying image-mask pairs to enhance existing segmentation tools. The core of MedSegFactory is a dual-stream diffusion model, where one stream synthesizes medical images and the other generates corresponding segmentation masks. To ensure precise alignment between image-mask pairs, we introduce Joint Cross-Attention (JCA), enabling a collaborative denoising paradigm by dynamic cross-conditioning between streams. This bidirectional interaction allows both representations to guide each other's generation, enhancing consistency between generated pairs. MedSegFactory unlocks on-demand generation of paired medical images and segmentation masks through user-defined prompts that specify the target labels, imaging modalities, anatomical regions, and pathological conditions, facilitating scalable and high-quality data generation. This new paradigm of medical image synthesis enables seamless integration into diverse medical imaging workflows, enhancing both efficiency and accuracy. Extensive experiments show that MedSegFactory generates data of superior quality and usability, achieving competitive or state-of-the-art performance in 2D and 3D segmentation tasks while addressing data scarcity and regulatory constraints.

Massimiliano Viola, Kevin Qu, Nando Metzger, Bingxin Ke, Alexander Becker, Konrad Schindler, Anton Obukhov

Depth completion upgrades sparse depth measurements into dense depth maps, guided by a conventional image. Existing methods for this highly ill-posed task operate in tightly constrained settings, and tend to struggle when applied to images outside the training domain, as well as when the available depth measurements are sparse, irregularly distributed, or of varying density. Inspired by recent advances in monocular depth estimation, we reframe depth completion as image-conditional depth map generation, guided by a sparse set of measurements. Our method, Marigold-DC, builds on a pretrained latent diffusion model (LDM) for depth estimation and injects the depth observations as test-time guidance, via an optimization scheme that runs in tandem with the iterative inference of denoising diffusion. The method exhibits excellent zero-shot generalization across a diverse range of environments and handles even extremely sparse guidance effectively. Our results suggest that contemporary monodepth priors greatly robustify depth completion: it may be better to view the task as recovering dense depth from (dense) image pixels, guided by sparse depth; rather than as inpainting (sparse) depth, guided by an image. Project website: https://MarigoldDepthCompletion.github.io/

Haoming Cai, Tsung-Wei Huang, Shiv Gehlot, Brandon Y. Feng, Sachin Shah, Guan-Ming Su, Christopher Metzler

Text-to-image diffusion models excel at generating diverse portraits, but lack intuitive shadow control. Existing editing approaches, as post-processing, struggle to offer effective manipulation across diverse styles. Additionally, these methods either rely on expensive real-world light-stage data collection or require extensive computational resources for training. To address these limitations, we introduce Shadow Director, a method that extracts and manipulates hidden shadow attributes within well-trained diffusion models. Our approach uses a small estimation network that requires only a few thousand synthetic images and hours of training--no costly real-world light-stage data needed. Shadow Director enables parametric and intuitive control over shadow shape, placement, and intensity during portrait generation while preserving artistic integrity and identity across diverse styles. Despite training only on synthetic data built on real-world identities, it generalizes effectively to generated portraits with diverse styles, making it a more accessible and resource-friendly solution.

Yifei Xia, Suhan Ling, Fangcheng Fu, Yujie Wang, Huixia Li, Xuefeng Xiao, Bin Cui

Generating high-quality long videos with Diffusion Transformers (DiTs) faces significant latency due to computationally intensive attention mechanisms. For instance, generating an 8s 720p video (110K tokens) with HunyuanVideo requires around 600 PFLOPs, with attention computations consuming about 500 PFLOPs.To tackle this, we propose AdaSpa, the first Dynamic Pattern and Online Precise Search sparse attention method for DiTs. First, AdaSpa uses a blockified pattern to efficiently represent the hierarchical sparsity inherent in DiTs, significantly reducing attention complexity while preserving video fidelity. This is motivated by our observation that DiTs' sparsity exhibits hierarchical and blockified structures across modalities.Second, AdaSpa introduces Fused LSE-Cached Search with Head-Adaptive Block Sparse Attention for efficient online precise search and computation. This approach leverages the invariance of sparse patterns and LSE across denoising steps, allowing precise real-time identification of sparse patterns with minimal overhead.AdaSpa is an adaptive, plug-and-play solution that seamlessly integrates into existing DiT models without additional training or data profiling. Extensive experiments validate that AdaSpa significantly accelerates video generation from 1.59x to 2.04x while maintaining video quality, demonstrating strong effectiveness.

Fan Li, Xuanbin Wang, Xuan Wang, Zhaoxiang Zhang, Yuelei Xu

Recently, open-vocabulary semantic segmentation has garnered growing attention. Most current methods leverage vision-language models like CLIP to recognize unseen categories through their zero-shot capabilities. However, CLIP struggles to establish potential spatial dependencies among scene objects due to its holistic pre-training objective, causing sub-optimal results. In this paper, we propose a DEnoising learning framework based on the Diffusion model for Open-vocabulary semantic Segmentation, called DEDOS, which is aimed at constructing the scene skeleton. Motivation stems from the fact that diffusion models incorporate not only the visual appearance of objects but also embed rich scene spatial priors. Our core idea is to view images as labels embedded with "noise"--non-essential details for perceptual tasks--and to disentangle the intrinsic scene prior from the diffusion feature during the denoising process of the images. Specifically, to fully harness the scene prior knowledge of the diffusion model, we introduce learnable proxy queries during the denoising process. Meanwhile, we leverage the robustness of CLIP features to texture shifts as supervision, guiding proxy queries to focus on constructing the scene skeleton and avoiding interference from texture information in the diffusion feature space. Finally, we enhance spatial understanding within CLIP features using proxy queries, which also serve as an interface for multi-level interaction between text and visual modalities. Extensive experiments validate the effectiveness of our method, experimental results on five standard benchmarks have shown that DEDOS achieves state-of-the-art performance. We will make the code publicly available.

Yihong Luo, Tianyang Hu, Yifan Song, Jiacheng Sun, Zhenguo Li, Jing Tang

While diffusion distillation has enabled one-step generation through methods like Variational Score Distillation, adapting distilled models to emerging *new controls* -- such as novel structural constraints or latest user preferences -- remains challenging. Conventional approaches typically requires modifying the base diffusion model and redistilling it -- a process that is both computationally intensive and time-consuming. To address these challenges, we introduce Joint Distribution Matching (JDM), a novel approach that minimizes the reverse KL divergence between image-condition joint distributions. By deriving a tractable upper bound, JDM decouples fidelity learning from condition learning. This asymmetric distillation scheme enables our one-step student to handle controls unknown to the teacher model and facilitates improved classifier-free guidance (CFG) usage and seamless integration of human feedback learning (HFL). Experimental results demonstrate that JDM surpasses baseline methods such as multi-step ControlNet by mere one-step in most cases, while achieving state-of-the-art performance in one-step text-to-image synthesis through improved usage of CFG or HFL integration.

Zhe Ma, Qingming Li, Xuhong Zhang, Tianyu Du, Ruixiao Lin, Zonghui Wang, Shouling Ji, Wenzhi Chen

The past few years have witnessed substantial advances in image generation powered by diffusion models. However, it was shown that diffusion models are susceptible to training data memorization, raising significant concerns regarding copyright infringement and privacy invasion. This study delves into a rigorous analysis of memorization in diffusion models. We introduce InvMM, an inversion-based measure of memorization, which is based on inverting a sensitive latent noise distribution accounting for the replication of an image. For accurate estimation of the measure, we propose an adaptive algorithm that balances the normality and sensitivity of the noise distribution. Comprehensive experiments across four datasets, conducted on both unconditional and text-guided diffusion models, demonstrate that InvMM provides a reliable and complete quantification of memorization. Notably, InvMM is commensurable between samples, reveals the true extent of memorization from an adversarial standpoint and implies how memorization differs from membership. In practice, it serves as an auditing tool for developers to reliably assess the risk of memorization, thereby contributing to the enhancement of trustworthiness and privacy-preserving capabilities of diffusion models.

Yu-Chien Liao, Jr-Jen Chen, Chi-Pin Huang, Ci-Siang Lin, Meng-Lin Wu, Yu-Chiang Frank Wang

Updating diffusion models in an incremental setting would be practical in real-world applications yet computationally challenging. We present a novel learning strategy of Concept Neuron Selection, a simple yet effective approach to perform personalization in a continual learning scheme. CNS uniquely identifies neurons in diffusion models that are closely related to the target concepts. In order to mitigate catastrophic forgetting problems while preserving zero-shot text-to-image generation ability, CNS finetunes concept neurons in an incremental manner and jointly preserves knowledge learned of previous concepts. Evaluation of real-world datasets demonstrates that CNS achieves state-of-the-art performance with minimal parameter adjustments, outperforming previous methods in both single and multi-concept personalization works. CNS also achieves fusion-free operation, reducing memory storage and processing time for continual personalization.

Fei Peng, Junqiang Wu, Yan Li, Tingting Gao, Di Zhang, Huiyuan Fu

Existing text-to-image diffusion models have demonstrated remarkable capabilities in generating high-quality images guided by textual prompts. However, achieving multi-subject compositional synthesis with precise spatial control remains a significant challenge. In this work, we address the task of layout-controllable multi-subject synthesis (LMS), which requires both faithful reconstruction of reference subjects and their accurate placement in specified regions within a unified image. While recent advancements have separately improved layout control and subject synthesis, existing approaches struggle to simultaneously satisfy the dual requirements of spatial precision and identity preservation in this composite task. To bridge this gap, we propose MUSE, a unified synthesis framework that employs concatenated cross-attention (CCA) to seamlessly integrate layout specifications with textual guidance through explicit semantic space expansion. The proposed CCA mechanism enables bidirectional modality alignment between spatial constraints and textual descriptions without interference. Furthermore, we design a progressive two-stage training strategy that decomposes the LMS task into learnable sub-objectives for effective optimization. Extensive experiments demonstrate that MUSE achieves zero-shot end-to-end generation with superior spatial accuracy and identity consistency compared to existing solutions, advancing the frontier of controllable image synthesis. Our code and model are available at https://github.com/pf0607/MUSE.

Junyu Chen, Dongyun Zou, Wenkun He, Junsong Chen, Enze Xie, Song Han, Han Cai

We present DC-AE 1.5, a new family of deep compression autoencoders for high-resolution diffusion models. Increasing the autoencoder's latent channel number is a highly effective approach for improving its reconstruction quality. However, it results in slow convergence for diffusion models, leading to poorer generation quality despite better reconstruction quality. This issue limits the quality upper bound of latent diffusion models and hinders the employment of autoencoders with higher spatial compression ratios. We introduce two key innovations to address this challenge: i) Structured Latent Space, a training-based approach to impose a desired channel-wise structure on the latent space with front latent channels capturing object structures and latter latent channels capturing image details; ii) Augmented Diffusion Training, an augmented diffusion training strategy with additional diffusion training objectives on object latent channels to accelerate convergence. With these techniques, DC-AE 1.5 delivers faster convergence and better diffusion scaling results than DC-AE. On ImageNet 512x512, DC-AE-1.5-f64c128 delivers better image generation quality than DC-AE-f32c32 while being 4x faster.

Jiayuan Lu, Rengan Xie, Zixuan Xie, Zhizhen Wu, Dianbing Xi, Qi Ye, Rui Wang, Hujun Bao, Yuchi Huo

Realistic images are usually produced by simulating light transportation results of 3D scenes using rendering engines. This framework can precisely control the output but is usually weak at producing photo-like images. Alternatively, diffusion models have seen great success in photorealistic image generation by leveraging priors from large datasets of real-world images but lack affordance controls. Promisingly, the recent ControlNet enables flexible control of the diffusion model without degrading its generation quality. In this work, we introduce IntrinsicControlNet, an intrinsically controllable image generation framework that enables easily generating photorealistic images from precise and explicit control, similar to a rendering engine, by using intrinsic images such as material properties, geometric details, and lighting as network inputs. Beyond this, we notice that there is a domain gap between the synthetic and real-world datasets, and therefore, naively blending these datasets yields domain confusion. To address this problem, we present a cross-domain control architecture that extracts control information from synthetic datasets, and control and content information from real-world datasets. This bridges the domain gap between real-world and synthetic datasets, enabling the blending or editing of 3D assets and real-world photos to support various interesting applications. Experiments and user studies demonstrate that our method can generate explicitly controllable and highly photorealistic images based on the input intrinsic images.

Chengyu Zheng, Jin Huang, Honghua Chen, Mingqiang Wei

Recent research leveraging large-scale pretrained diffusion models has demonstrated the potential of using diffusion features to establish semantic correspondences in images. Inspired by advancements in diffusion-based techniques, we propose a novel zero-shot method for refining point cloud registration algorithms. Our approach leverages correspondences derived from depth images to enhance point feature representations, eliminating the need for a dedicated training dataset. Specifically, we first project the point cloud into depth maps from multiple perspectives and extract implicit knowledge from a pretrained diffusion network as depth diffusion features. These features are then integrated with geometric features obtained from existing methods to establish more accurate correspondences between point clouds. By leveraging these refined correspondences, our approach achieves significantly improved registration accuracy. Extensive experiments demonstrate that our method not only enhances the performance of existing point cloud registration techniques but also exhibits robust generalization capabilities across diverse datasets. Codes are available a https://github.com/zhengcy-lambo/RARE.git.

Ying Guo, Xi Liu, Cheng Zhen, Pengfei Yan, Xiaoming Wei

Face-to-face communication, as a common human activity, motivates the research on interactive head generation. A virtual agent can generate motion responses with both listening and speaking capabilities based on the audio or motion signals of the other user and itself. However, previous clip-wise generation paradigm or explicit listener/speaker generator-switching methods have limitations in future signal acquisition, contextual behavioral understanding, and switching smoothness, making it challenging to be real-time and realistic.In this paper, we propose an autoregressive (AR) based frame-wise framework called ARIG to realize the real-time generation with better interaction realism. To achieve real-time generation, we model motion prediction as a non-vector-quantized AR process. Unlike discrete codebook-index prediction, we represent motion distribution using diffusion procedure, achieving more accurate predictions in continuous space. To improve interaction realism, we emphasize interactive behavior understanding (IBU) and detailed conversational state understanding (CSU). In IBU, based on dual-track dual-modal signals, we summarize short-range behaviors through bidirectional-integrated learning and perform contextual understanding over long ranges. In CSU, we use voice activity signals and context features of IBU to understand the various states (interruption, feedback, pause, etc.) that exist in actual conversations. These serve as conditions for the final progressive motion prediction. Extensive experiments have verified the effectiveness of our model.

Zheng-Peng Duan, Jiawei Zhang, Xin Jin, Ziheng Zhang, Zheng Xiong, Dongqing Zou, Jimmy S. Ren, Chunle Guo, Chongyi Li

Large-scale pre-trained diffusion models are becoming increasingly popular in solving the Real-World Image Super-Resolution (Real-ISR) problem because of their rich generative priors. The recent development of diffusion transformer (DiT) has witnessed overwhelming performance over the traditional UNet-based architecture in image generation, which also raises the question: Can we adopt the advanced DiT-based diffusion model for Real-ISR?To this end, we propose our DiT4SR, one of the pioneering works to tame the large-scale DiT model for Real-ISR.Instead of directly injecting embeddings extracted from low-resolution (LR) images like ControlNet, we integrate the LR embeddings into the original attention mechanism of DiT, allowing for the bidirectional flow of information between the LR latent and the generated latent. The sufficient interaction of these two streams allows the LR stream to evolve with the diffusion process, producing progressively refined guidance that better aligns with the generated latent at each diffusion step. Additionally, the LR guidance is injected into the generated latent via a cross-stream convolution layer, compensating for DiT's limited ability to capture local information. These simple but effective designs endow the DiT model with superior performance in Real-ISR, which is demonstrated by extensive experiments.

Zhengkang Xiang, Zizhao Li, Amir Khodabandeh, Kourosh Khoshelham

Lidar point cloud synthesis based on generative models offers a promising solution to augment deep learning pipelines, particularly when real-world data is scarce or lacks diversity. By enabling flexible object manipulation, this synthesis approach can significantly enrich training datasets and enhance discriminative models. However, existing methods focus on unconditional lidar point cloud generation, overlooking their potential for real-world applications. In this paper, we propose SG-LDM, a Semantic-Guided Lidar Diffusion Model that employs latent alignment to enable robust semantic-to-lidar synthesis. By directly operating in the native lidar space and leveraging explicit semantic conditioning, SG-LDM achieves state-of-the-art performance in generating high-fidelity lidar point clouds guided by semantic labels. Moreover, we propose the first diffusion-based lidar translation framework based on SG-LDM, which enables cross-domain translation as a domain adaptation strategy to enhance downstream perception performance. Systematic experiments demonstrate that SG-LDM significantly outperforms existing lidar diffusion models and the proposed lidar translation framework further improves data augmentation performance in the downstream lidar segmentation task.

Wenda Shi, Yiren Song, Dengming Zhang, Jiaming Liu, Xingxing Zou

Visual text rendering are widespread in various real-world applications, requiring careful font selection and typographic choices. Recent progress in diffusion transformer (DiT)-based text-to-image (T2I) models show promise in automating these processes. However, these methods still encounter challenges like inconsistent fonts, style variation, and limited fine-grained control, particularly at the word-level. This paper proposes a two-stage DiT-based pipeline to address these problems by enhancing controllability over typography and style in text rendering. We introduce typography control fine-tuning (TC-FT), an parameter-efficient fine-tuning method (on 5% key parameters) with enclosing typography control tokens (ETC-tokens), which enables precise word-level application of typographic features. To further address style inconsistency in text rendering, we propose a text-agnostic style control adapter (SCA) that prevents content leakage while enhancing style consistency. To implement TC-FT and SCA effectively, we incorporated HTML-render into the data synthesis pipeline and proposed the first word-level controllable dataset. Through comprehensive experiments, we demonstrate the effectiveness of our approach in achieving superior word-level typographic control, font consistency, and style consistency in text rendering tasks. Our project page is available at this site.

Xiang Lv, Mingwen Shao, Lingzhuang Meng, Chang Liu, Yecong Wan, Xinyuan Chen

Recently, text-driven diffusion models have significantly promoted the development of video editing. However, there still remain two practical challenges: (1) existing text-to-video editing methods struggle to understand negative text prompt, resulting in ineffective suppression of undesirable content in edited video; (2) these methods are difficult to maintain the temporal consistency of the edited video, leading to inter-frame flickering. To address the above challenges, we propose SUV, a novel semantic modulation method based on text embeddings to suppress undesired content in the edited video. Specifically, on the one hand, we discover that the end embeddings (EE) contain substantial coupled positive and negative embeddings, which is the primary reason for the appearance of undesirable content in the edited video. Based on this discovery, we advocate decoupling the negative embeddings from the EE by employing singular value decomposition and propose an exponential suppression operator to decrease the singular values of negative embeddings, thereby restraining the effect of negative embeddings on the edited video content. Subsequently, two constraints are designed to further suppress negative content while keep positive content unchanged via pushing negative embeddings apart and pulling positive embeddings closer. On the other hand, to boost the temporal consistency of edited video, we devise a fuzzy feature selection strategy to fuse similar features in different frame for avoiding inter-frame flickering. Benefiting from the above elaborate designs, our method not only effectively suppresses undesired content of video, but also maintains inter-frame consistency. Extensive experiments demonstrate that our SUV significantly improves edit accuracy and temporal consistency of edited videos compared to existing methods.

Yuanzhi Zhu, Xi Wang, Stéphane Lathuilière, Vicky Kalogeiton

Masked Diffusion Models (MDMs) have emerged as a powerful generative modeling technique. Despite their remarkable results, they typically suffer from slow inference with several steps. In this paper, we propose Di\mathtt [M] O, a novel approach that distills masked diffusion models into a one-step generator.Di\mathtt [M] O addresses two key challenges: (1) the intractability of using intermediate-step information for one-step generation, which we solve through token-level distribution matching that optimizes model output logits by an `on-policy framework' with the help of an auxiliary model; and (2) the lack of entropy in the initial distribution, which we address through a token initialization strategy that injects randomness while maintaining similarity to teacher training distribution. We show Di\mathtt [M] O's effectiveness on both class-conditional and text-conditional image generation, impressively achieving performance competitive to multi-step teacher outputs while drastically reducing inference time. To our knowledge, we are the first to successfully achieve one-step distillation of masked diffusion models and the first to apply discrete distillation to text-to-image generation, opening new paths for efficient generative modeling.

Yujie Zhou, Jiazi Bu, Pengyang Ling, Pan Zhang, Tong Wu, Qidong Huang, Jinsong Li, Xiaoyi Dong, Yuhang Zang, Yuhang Cao 等

Recent advancements in image relighting models, driven by large-scale datasets and pre-trained diffusion models, have enabled the imposition of consistent lighting. However, video relighting still lags, primarily due to the excessive training costs and the scarcity of diverse, high-quality video relighting datasets. A simple application of image relighting models on a frame-by-frame basis leads to several issues: lighting source inconsistency and relighted appearance inconsistency, resulting in flickers in the generated videos. In this work, we propose Light-A-Video, a training-free approach to achieve temporally smooth video relighting. Adapted from image relighting models, Light-A-Video introduces two key techniques to enhance lighting consistency. First, we design a Consistent Light Attention (CLA) module, which enhances cross-frame interactions within the self-attention layers of the image relight model to stabilize the generation of the background lighting source. Second, leveraging the physical principle of light transport independence, we apply linear blending between the source video's appearance and the relighted appearance, using a Progressive Light Fusion (PLF) strategy to ensure smooth temporal transitions in illumination. Experiments show that Light-A-Video improves the temporal consistency of relighted video while maintaining the relighted image quality, ensuring coherent lighting transitions across frames.