This paper addresses a promising yet underexplored task, Image-to-Sounding-Video (I2SV) generation, which animates a static image and generates synchronized sound simultaneously. Despite advances in video and audio generation models, challenges remain to develop a unified model for generating naturally sounding videos. In this work, we propose a novel approach that leverages two separate pretrained diffusion models and makes vision and audio influence each other during generation based on the Diffusion Transformer (DiT) architecture. First, the individual video and audio pretrained generation models are decomposed into input, output, and expert sub-modules. We propose using a unified joint DiT block to integrate the expert sub-modules to effectively model the interaction between the two modalities, resulting in high-quality I2SV generation. Then, we introduce a joint classifier-free guidance technique to boost the performance during joint generation. Finally, we conduct extensive experiments on three popular benchmark datasets, and in both objective and subjective evaluation our method surpass all the baseline methods in almost all metrics. Case studies show our generated sounding videos are high quality and synchronized between video and audio.
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Rolling shutter (RS) cameras dominate consumer and smartphone markets. Several methods for computing the absolute pose of RS cameras have appeared in the last 20 years, but the relative pose problem has not been fully solved yet. We provide a unified theory for the important class of order-one rolling shutter (RS_1) cameras. These cameras generalize the perspective projection to RS cameras, projecting a generic space point to exactly one image point via a rational map. We introduce a new back-projection RS camera model, characterize RS_1 cameras, construct explicit parameterizations of such cameras, and determine the image of a space line. We classify all minimal problems for solving the relative camera pose problem with linear RS_1 cameras and discover new practical cases. Finally, we show how the theory can be used to explain RS models previously used for absolute pose computation.
Text-to-image (T2I) models have rapidly advanced, enabling the generation of high-quality images from text prompts across various domains. However, these models present notable safety concerns, including the risk of generating harmful, biased, or private content. Current research on assessing T2I safety remains in its early stages. While some efforts have been made to evaluate models on specific safety dimensions, many critical risks remain unexplored. To address this gap, we introduce T2ISafety, a safety benchmark that evaluates T2I models across three key domains: toxicity, fairness, and bias. We build a detailed hierarchy of 12 tasks and 44 categories based on these three domains, and meticulously collect 70K corresponding prompts. Based on this taxonomy and prompt set, we build a large-scale T2I dataset with 68K manually annotated images and train an evaluator capable of detecting critical risks that previous work has failed to identify, including risks that even ultra-large proprietary models like GPTs cannot correctly detect. We evaluate 15 prominent diffusion models on T2ISafety and reveal several concerns including persistent issues with racial fairness, a tendency to generate toxic content, and significant variation in privacy protection across the models, even with defense methods like concept erasing.
Text-to-video (T2V) generation has made tremendous progress in generating complicated scenes based on texts. However, human-object interaction (HOI) often cannot be precisely generated by current T2V models due to the lack of large-scale videos with accurate captions for HOI. To address this issue, we introduce HOIGen-1M, the first large-scale dataset for HOI Generation, consisting of over one million high-quality videos collected from diverse sources. In particular, to guarantee the high quality of videos, we first design an efficient framework to automatically curate HOI videos using the powerful multimodal large language models (MLLMs), and then the videos are further cleaned by human annotators. Moreover, to obtain accurate textual captions for HOI videos, we design a novel video description method based on a Mixture-of-Multimodal-Experts (MoME) strategy that not only generates expressive captions but also eliminates the hallucination by individual MLLM. Furthermore, due to the lack of an evaluation framework for generated HOI videos, we propose two new metrics to assess the quality of generated videos in a coarse-to-fine manner. Extensive experiments reveal that current T2V models struggle to generate high-quality HOI videos and confirm that our HOIGen-1M dataset is instrumental for improving HOI video generation.
The success of text-to-image generation enabled by diffusion models has imposed an urgent need to erase unwanted concepts, e.g., copyrighted, offensive, and unsafe ones, from the pre-trained models in a precise, timely, and low-cost manner. The twofold demand of concept erasure requires a precise removal of the target concept during generation (i.e., erasure efficacy), while a minimal impact on non-target content generation (i.e., prior preservation). Existing methods are either computationally costly or face challenges in maintaining an effective balance between erasure efficacy and prior preservation. To improve, we propose a precise, fast, and low-cost concept erasure method, called Adaptive Vaule Decomposer (AdaVD), which is training-free. This method is grounded in a classical linear algebraic orthogonal complement operation, implemented in the value space of each cross-attention layer within the UNet of diffusion models. An effective shift factor is designed to adaptively navigate the erasure strength, enhancing prior preservation without sacrificing erasure efficacy. Extensive experimental results show that the proposed AdaVD is effective at both single and multiple concept erasure, showing a 2- to 10-fold improvement in prior preservation as compared to the second best, meanwhile achieving the best or near best erasure efficacy, when comparing with both training-based and training-free state of the arts. AdaVD supports a series of diffusion models and downstream image generation tasks, with code to be publicly available.
Human hands play a central role in interacting, motivating increasing research in dexterous robotic manipulation. Data-driven embodied AI algorithms demand precise, large-scale, human-like manipulation sequences, which are challenging to obtain with conventional reinforcement learning or real-world teleoperation. To address this, we introduce ManipTrans, a novel two-stage method for efficiently transferring human bimanual skills to dexterous robotic hands in simulation. ManipTrans first pre-trains a generalist trajectory imitator to mimic hand motion, then fine-tunes a specific residual module under interaction constraints, enabling efficient learning and accurate execution of complex bimanual tasks. Experiments show that ManipTrans surpasses state-of-the-art methods in success rate, fidelity, and efficiency. Leveraging ManipTrans, we transfer multiple hand-object datasets to robotic hands, creating DexManipNet, a large-scale dataset featuring previously unexplored tasks like pen capping and bottle unscrewing. DexManipNet comprises 3.3K episodes of robotic manipulation and is easily extensible, facilitating further policy training for dexterous hands and enabling real-world deployments.
Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning
PDF ↗Three key challenges hinder the development of current deepfake video detection: (1) Temporal features can be complex and diverse: how can we identify general temporal artifacts to enhance model generalization? (2) Spatiotemporal models often lean heavily on one type of artifact and ignore the other: how can we ensure balanced learning from both? (3) Videos are naturally resource-intensive: how can we tackle efficiency without compromising accuracy? This paper attempts to tackle the three challenges jointly. First, inspired by the notable generality of using image-level blending data for image forgery detection, we investigate whether and how video-level blending can be effective in video. We then perform a thorough analysis and identify a previously underexplored temporal forgery artifact: Facial Feature Drift (FFD), which commonly exists across different forgeries. To reproduce FFD, we then propose a novel Video-level Blending data (VB), where VB is implemented by blending the original image and its warped version frame-by-frame, serving as a hard negative sample to mine more general artifacts. Second, we carefully design a lightweight Spatiotemporal Adapter (StA) to equip a pre-trained image model with the ability to capture both spatial and temporal features jointly and efficiently. StA is designed with two-stream 3D-Conv with varying kernel sizes, allowing it to process spatial and temporal features separately. This eliminates the need to design a new deepfake-specific video architecture from scratch. Extensive experiments validate the effectiveness of the proposed methods; and show our approach can generalize well to previously unseen forgery videos.
3D Gaussian splatting (3DGS) has enabled various applications in 3D scene representation and novel view synthesis due to its efficient rendering capabilities. However, 3DGS demands significant GPU memory, limiting its use on devices with restricted computational resources. Previous approaches have focused on pruning less important Gaussians, effectively compressing 3DGS but often requiring a fine-tuning stage and lacking adaptability for the specific memory needs of different devices. In this work, we present an elastic inference method for 3DGS. Given an input for the desired model size, our method selects and transforms a subset of Gaussians, achieving substantial rendering performance without additional fine-tuning. We introduce a tiny learnable module that controls Gaussian selection based on the input percentage, along with a transformation module that adjusts the selected Gaussians to complement the performance of the reduced model. Comprehensive experiments on ZipNeRF, MipNeRF and Tanks&Temples scenes demonstrate the effectiveness of our approach. Code will be publicly available.
We present CaricatureBooth, a system that transforms caricature creation into a simple interactive experience -- as easy as using a photo booth! A key challenge in caricature generation is two-fold: the scarcity of high-quality caricature data and the difficulty in enabling precise creative control over the exaggeration process while maintaining identity. Prior approaches either require large-scale caricature and photo data or lack intuitive mechanisms for users to guide the deformation without losing identity. We address the data scarcity by synthesising training data through Thin Plate Spline (TPS) deformation of standard face images. For creative control, we design a Bezier curve interface where users can easily manipulate facial features, with these edits then driving TPS transformations at inference time. When combined with a pre-trained ID-preserving diffusion model, our system maintains both identity preservation and creative flexibility. Through extensive experiments, we demonstrate that CaricatureBooth achieves state-of-the-art quality while making the joy of caricature creation as accessible as taking a photo -- just walk in and walk out with your personalised caricature! Code is available at https://github.com/WinKawaks/CaricatureBooth.
Implicit Neural Representations (INRs) are increasingly recognized as a versatile data modality for representing discretized signals, offering benefits such as infinite query resolution and reduced storage requirements. Existing signal compression approaches for INRs typically employ one of two strategies: 1. direct quantization with entropy coding of the trained INR; 2. deriving a latent code on top of the INR through a learnable transformation. Thus, their performance is heavily dependent on the quantization and entropy coding schemes employed. In this paper, we introduce SINR, an innovative compression algorithm that leverages the patterns in the vector spaces formed by weights of INRs. We compress these vector spaces using a high-dimensional sparse code within a dictionary. Further analysis reveals that the atoms of the dictionary used to generate the sparse code do not need to be learned or transmitted to successfully recover the INR weights. We demonstrate that the proposed approach can be integrated with any existing INR-based signal compression technique. Our results indicate that SINR achieves substantial reductions in storage requirements for INRs across various configurations, outperforming conventional INR-based compression baselines. Furthermore, SINR maintains high-quality decoding across diverse data modalities, including images, occupancy fields, and Neural Radiance Fields.
FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning
PDF ↗Server aggregation conflict is a key challenge in personalized federated learning (PFL). While existing PFL methods have achieved significant progress with shallow base models (e.g., four-layer CNNs), they often overlook the negative impacts of deeper base models on personalization mechanisms. In this paper, we identify the phenomenon of deep model degradation in PFL, where as base model depth increases, the model becomes more sensitive to local client data distributions, thereby exacerbating server aggregation conflicts and ultimately reducing overall model performance. Moreover, we show that these conflicts manifest in insufficient global average updates and mutual constraints between clients. Motivated by our analysis, we proposed a two-stage conflict-aware layer-wise mitigation algorithm (FedCALM), which first constructs a conflict-free global update to alleviate negative conflicts, and then maximizes the benefits of all clients through a conflict-aware strategy. Notably, our method naturally leads to a selective mechanism that balances the tradeoff between clients involved in aggregation and the tolerance for conflicts. Consequently, it can boost the positive contribution to the clients even with the greatest conflicts with the global update. Extensive experiments across multiple datasets and deeper base models demonstrate that FedCALM outperforms four state-of-the-art (SOTA) methods by up to 9.88% and seamlessly integrates into existing PFL methods with performance improvements of up to 9.01%.
Text- or image-to-3D generators and 3D scanners can now produce 3D assets with high-quality shapes and textures, but as single, fused entities lacking meaningful structure. In contrast, most applications and creative workflows require 3D assets to be composed of distinct, meaningful parts that can be independently manipulated. To bridge this gap, we introduce PartGen, a novel approach for generating, from text, images, or unstructured 3D objects, 3D objects composed of meaningful parts. Our method leverages a multi-view diffusion model to extract plausible and view-consistent part segmentations from multiple views of a 3D object, dividing it into meaningful components. A second multi-view diffusion model then processes each part individually, filling in occlusions and generating completed views, which are subsequently passed to a 3D reconstruction network. The completion process ensures that the reconstructed parts integrate cohesively by considering the context of the entire object, compensating for missing information caused by occlusions and, in extreme cases, hallucinating entirely invisible parts based on contextual cues. We evaluate PartGen on both generated and real 3D assets, demonstrating significant improvements over segmentation and part completion baselines. We also showcase downstream applications such as text-guided 3D part editing.
Recent methods have made significant progress in optical flow estimation. However, the evaluation of these methods focuses mainly on improved accuracy in benchmarks and often overlooks the analysis of network robustness, which may be important in safety-critical scenarios such as autonomous driving. In this paper, we propose a novel method for robustness evaluation by modifying data from original benchmarks. Unlike previous benchmarks that focus on complex scenes, we propose to modify shape and texture of objects from the original images in order to analyze the sensitivity to these changes observed in the output. Our aim is to identify common failure cases of state-of-the-art (SOTA) methods to evaluate their robustness and understand their behaviors. We show that: Optical flow methods are more sensitive to shape changes than to texture changes; and optical flow methods tend to "remember" objects seen during training and may "ignore" the motion of unseen objects. Our experimental results and findings provide a more in-depth understanding of the behavior of recent optical flow methods.
Online stereo rectification is critical for autonomous vehicles and robots in dynamic environments, where factors such as vibration, temperature fluctuations, and mechanical stress can affect rectification accuracy and severely degrade downstream stereo depth estimation. Current dominant approaches for online stereo rectification involve estimating relative camera poses in real time to derive rectification homographies. However, they do not directly optimize for rectification constraints. Additionally, the general-purpose correspondence matchers used in these methods are not trained for rectification, while training of these matchers typically requires ground-truth correspondences which are not available in stereo rectification datasets. To address these limitations, we propose a matching-based stereo rectification framework that is directly optimized for rectification and does not require ground-truth correspondence annotations for training. We assume intrinsics are known as they are generally available on modern devices and are relatively stable. Our framework incorporates a rectification-constrained estimator and applies multi-level, rectification-specific supervision that trains the matcher network for rectification without relying on ground-truth correspondences. Additionally, we create a new rectification dataset with ground-truth optical flow annotations, eliminating bias from evaluation metrics used in prior work that relied on pretrained keypoint matching or optical flow models. Extensive experiments show that our approach outperforms both state-of-the-art matching-based and matching-free methods in vertical flow metric by 10.7% on the Carla-Flowguided dataset and 21.3% on the Semi-Truck Highway dataset, offering superior rectification accuracy.
Model editing aims to correct inaccurate knowledge, update outdated information, and incorporate new data into Large Language Models (LLMs) without the need for retraining. This task poses challenges in lifelong scenarios where edits must be continuously applied for real-world applications. While some editors demonstrate strong robustness for lifelong editing in pure LLMs, Vision LLMs (VLLMs), which incorporate an additional vision modality, are not directly adaptable to existing LLM editors. In this paper, we propose LiveEdit, a lifelong vision language model edit to bridge the gap between lifelong LLM editing and VLLMs. We begin by training an editing expert generator to independently produce low-rank experts for each editing instance, with the goal of correcting the relevant responses of the VLLM. A hard filtering mechanism is developed to utilize visual semantic knowledge, thereby coarsely eliminating visually irrelevant experts for input queries during the inference stage of the post-edited model. Finally, to integrate visually relevant experts, we introduce a soft routing mechanism based on textual semantic relevance to achieve multi-expert fusion. For evaluation, we establish a benchmark for lifelong VLLM editing. Extensive experiments demonstrate that LiveEdit offers significant advantages in lifelong VLLM editing scenarios. Further experiments validate the rationality and effectiveness of each module design in LiveEdit.
Knowledge distillation (KD) has been a popular and effective method for model compression. One important assumption of KD is that the teacher's original dataset will also be available when training the student. However, in situations such as continual learning and distilling large models trained on company-withheld datasets, having access to the original data may not always be possible. This leads practitioners towards utilizing other sources of supplemental data, which could yield mixed results. One must then ask: "what makes a good dataset for transferring knowledge from teacher to student?" Many would assume that only real in-domain imagery is viable, but is that the only option? In this work, we explore multiple possible surrogate distillation datasets and demonstrate that many different datasets, even unnatural synthetic imagery, can serve as a suitable alternative in KD. From examining these alternative datasets, we identify and present various criteria describing what makes a good dataset for distillation. Source code is available at https://github.com/osu-cvl/good-kd-dataset.
This paper proposes the first video-grounded entailment tree reasoning method for commonsense video question answering (VQA). Despite the remarkable progress of large visual-language models (VLMs), there are growing concerns that they learn spurious correlations between videos and likely answers, reinforced by their black-box nature and remaining benchmarking biases. Our method explicitly grounds VQA tasks to video fragments in four steps: entailment tree construction, video-language entailment verification, tree reasoning, and dynamic tree expansion. A vital benefit of the method is its generalizability to current video- and image-based VLMs across reasoning types.To support fair evaluation, we devise a de-biasing procedure based on large-language models that rewrite VQA benchmark answer sets to enforce model reasoning. Systematic experiments on existing and de-biased benchmarks highlight the impact of our method components across benchmarks, VLMs, and reasoning types.
L-SWAG: Layer-Sample Wise Activation with Gradients Information for Zero-Shot NAS on Vision Transformers
PDF ↗Training-free Neural Architecture Search (NAS) efficiently identifies high-performing neural networks using zero-cost (ZC) proxies. Unlike multi-shot and one-shot NAS approaches, ZC-NAS is both (i) time-efficient, eliminating the need for model training, and (ii) interpretable, with proxy designs often theoretically grounded. Despite rapid developments in the field, current SOTA ZC proxies are typically constrained to well-established convolutional search spaces. With the rise of Large Language Models shaping the future of deep learning, this work extends ZC proxy applicability to Vision Transformers (ViTs). We present a new benchmark using the Autoformer search space evaluated on 6 distinct tasks, and propose Layer-Sample Wise Activation with Gradients information (L-SWAG), a novel, generalizable metric that characterises both convolutional and transformer architectures across 14 tasks. Additionally, previous works highlighted how different proxies contain complementary information, motivating the need for a ML model to identify useful combinations. To further enhance ZC-NAS, we therefore introduce LIBRA-NAS (Low Information gain and Bias Re-Alignment), a method that strategically combines proxies to best represent a specific benchmark. Integrated into the NAS search, LIBRA-NAS outperforms evolution and gradient-based NAS techniques by identifying an architecture with a 17.0% test error on ImageNet1k in just 0.1 GPU days.
Scene Text Retrieval (STR) seeks to identify all images containing a given query string. Existing methods typically rely on an explicit Optical Character Recognition (OCR) process of text spotting or localization, which is susceptible to complex pipelines and accumulated errors. To settle this, we resort to the Contrastive Language-Image Pre-training (CLIP) models, which have demonstrated the capacity to perceive and understand scene text, making it possible to achieve strictly OCR-free STR. From the perspective of parameter-efficient transfer learning, a lightweight visual position adapter is proposed to provide a positional information complement for the visual encoder. Besides, we introduce a visual context dropout technique to improve the alignment of local visual features. A novel, parameter-free cross-attention mechanism transfers the contrastive relationship between images and text to that between visual tokens and text, producing a rich cross-modal representation, which can be utilized for efficient reranking with a linear classifier. The resulting model, CAYN, which proves that CLIP is Almost all You Need for STR with no more than 0.50M additional parameters required, achieves new state-of-the-art performance on the STR task, with 92.46%/89.49%/85.98% mAP on the SVT/IIIT-STR/TTR datasets. Our findings demonstrate that CLIP can serve as a reliable and efficient solution for OCR-free STR.
Generalizable object keypoint localization is a fundamental computer vision task in understanding the object structure. It is challenging for existing keypoint localization methods because their limited training data cannot provide generalizable shape and semantic cues, leading to inferior performance and generalization capability. Instead of relying on large scale training data, this work tackles this challenge by exploiting the rich priors from large generative models. We propose a data-efficient generalizable localization method named GenLoc. GenLoc extracts the generative priors from a pre-trained image generation model by calculating the correlation map between image latent feature and condition embedding. Those priors are hence optimized with our proposed heatmap expectation loss to perform object keypoint localization. Benefited by the rich knowledge of generative priors in understanding of object semantics and structures, GenLoc achieves superior performance on various object keypoint localization benchmarks. It shows more substantial performance enhancements in cross-domain, few-shot and zero-shot evaluation settings, e.g., getting 20%+ AP enhancement over CLAMP in various zero-shot settings.