Human animation strives to bring static characters to life. Existing methods produce high-quality outcomes for single-frame animation; however, they often fail to maintain satisfactory temporal consistency, especially in facial and hand movements. This limitation arises from commonly used motion modules that do not explicitly model inter-entity relationships. In this work, we introduce MoAnimate, a Motion-oriented Human Animation framework designed to improve inter-entity consistency. Specifically, we extract motion flows from driving videos and transfer them to align the shape of character. During initialization, we propose a motion-oriented latent refinement that optimizes low-frequency subbands to regulate the layout of visual objects along flow trajectories, while preserving random high-frequency subbands to accommodate appearance variations. During denoising, we further introduce a motion-oriented entity attention module to enable direct and efficient interaction among entities within a coordinated subspace. Extensive experiments demonstrate that our method significantly enhances temporal consistency, particularly the visual consistency of the entities.

Diffusion transformers have shown exceptional performance in visual generation but incur high computational costs. Token reduction techniques that compress models by sharing the denoising process among similar tokens have been introduced. However, existing approaches neglect the denoising priors of the diffusion models, leading to suboptimal acceleration and diminished image quality. This study proposes a novel concept: attend to prune feature redundancies in areas not attended by the diffusion process. We analyze the location and degree of feature redundancies based on the structure-then-detail denoising priors. Subsequently, we introduce SDTM, a structure-then-detail token merging approach that dynamically compresses feature redundancies. Specifically, we design dynamic visual token merging, compression ratio adjusting, and prompt reweighting for different stages. Served in a post-training way, the proposed method can be integrated seamlessly into any DiT architecture. Extensive experiments across various backbones, schedulers, and datasets showcase the superiority of our method, for example, it achieves 1.55× acceleration with negligible impact on image quality. Project page: https://github.com/ICTMCG/SDTM.

Token reduction is an efficient method for accelerating vision transformers. Techniques like token pruning and merging progressively decrease the number of active tokens to reduce the computation cost. However, when applied to dense prediction tasks, these techniques crudely cache low-level features or unfold non-self features to reconstruct the inactive tokens. This hard replacement results in significant information loss, especially at higher compression ratios. In this work, we propose FR2ViT, a finetuning-free refinement-reactivation architecture for efficient vision transformers, allowing tokens of varying informativeness to activate softly at differing costs. Specifically, we design a high-order informativeness assessment to partition tokens into superior and inferior categories accurately. We refine superior tokens through extensive attention interaction, while inferior tokens undergo effective reactivation, and we establish a lightweight interaction between two branches. Our approach ensures continuous and autonomous interaction of each token, and is finetuning-free to easily accommodate the large model era. Experiments across three dense prediction tasks demonstrate our method’s exceptional performance. For example, FR2ViT achieves a 1.95× FPS with only a 0.43% performance drop for Segmenter ViT-L.

Considering that image editing and manipulation technologies pose significant threats to the authenticity and security of image content, research on image regional manipulation detection has always been a critical issue. The accelerated advancement of generative AI significantly enhances the viability and effectiveness of generative regional editing methods and has led to their gradual replacement of traditional image editing tools or algorithms. However, current research primarily focuses on traditional image tampering, and there remains a lack of a comprehensive dataset containing images edited with abundant generative regional editing methods. We endeavor to fill this vacancy by constructing the GRE dataset, a large-scale generative regional editing detection dataset with the following advantages: 1) Integration of a logical and simulated editing pipeline, leveraging multiple large models in various modalities. 2) Inclusion of various editing approaches with distinct characteristics. 3) Provision of comprehensive benchmark and evaluation of SOTA methods across related domains. 4) Analysis of the GRE dataset from multiple dimensions including necessity, rationality, and diversity. Extensive experiments and in-depth analysis demonstrate that this larger and more comprehensive dataset will significantly enhance the development of detection methods for generative editing. The corresponding repository is at https://github.com/ICTMCG/GRE.