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- PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment1,540
This paper tackles the challenging few-shot segmentation problem from a metric learning perspective and presents PANet, a novel prototype alignment network to better utilize the information of the support set to better generalize to unseen object categories.
- MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model426
This work introduces Magic/snimate, a diffusion-based framework that aims at enhancing temporal consistency, preserving reference image faithfully, and improving animation fidelity, and outperforms the strongest baseline on the challenging TikTok dancing dataset.
- DragDiffusion: Harnessing Diffusion Models for Interactive Point-Based Image Editing393
This work introduces two additional techniques, namely identity-preserving fine-tuning and reference-latent-control, to further preserve the identity of the original image and presents a challenging benchmark dataset called DRAGBENCH─ the first benchmark to evaluate the performance of interactive point-based image editing methods.
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- MagicEdit: High-Fidelity and Temporally Coherent Video Editing83
It is found that high-fidelity and temporally coherent video-to-video translation can be achieved by explicitly disentangling the learning of content, structure and motion signals during training.
- MagicMix: Semantic Mixing with Diffusion Models82
MagicMix is presented, a simple yet effective solution based on pre-trained text-conditioned diffusion models that does not require any spatial mask or re-training, yet is able to synthesize novel objects with high fidelity.
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- Body Meshes as Points74
A new method that represents multiple person instances as points in the spatial-depth space where each point is associated with one body mesh, Body Meshes as Points (BMP), to simplify the pipeline and lift both efficiency and performance.
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- Global Knowledge Calibration for Fast Open-Vocabulary Segmentation64
A core idea of preserving the generalizable representation when fine-tuning on known classes is proposed, and a text diversification strategy that generates a set of synonyms for each training category is introduced, which prevents the learned representation from collapsing onto specific known category names.
- DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention63
This paper aims to incorporate the sub-quadratic modeling capability of Gated Linear Attention (GLA) into the 2D diffusion backbone, and introduces Diffusion Gated Linear Attention Transformers (DiG), a simple, adoptable solution with minimal parameter overhead.
- MagicVideo-V2: Multi-Stage High-Aesthetic Video Generation63
This work introduces MagicVideo-V2 that integrates the text-to-image model, video motion generator, reference image embedding module and frame interpolation module into an end-to-end video generation pipeline that can generate an aesthetically pleasing, high-resolution video with remarkable fidelity and smoothness.
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- SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process57
A model-agnostic solution called SegRefiner is proposed, which offers a novel perspective on segmentation refinement as a data generation process by interpreting segmentation refinement as a data generation process, and which outperforms previous model-agnostic refinement methods by a significant margin.
- Focus, Segment and Erase: An Efficient Network for Multi-label Brain Tumor Segmentation56
This work proposes a novel end-to-end trainable network named FSENet, which has a tumor region pooling component to restrict the prediction within the tumor region (“focus”), thus mitigating the influence of the dominant non-tumor region.
- MagicArticulate: Make Your 3D Models Articulation-Ready50
Extensive experiments demonstrate that MagicArticulate significantly outperforms existing methods across diverse object categories, achieving high-quality articulation that enables realistic animation.
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- The Scalability of Simplicity: Empirical Analysis of Vision-Language Learning with a Single Transformer34
Sail is introduced, a single transformer unified multimodal large language model (MLLM) that integrates raw pixel encoding and language decoding within a singular architecture that eliminates the need for a separate vision encoder, presenting a more minimalist architecture design.
- PV3D: A 3D Generative Model for Portrait Video Generation28
Pv3D is proposed, the first generative framework that can synthesize multi-view consistent portrait videos with high-quality appearance and geometry and is able to support many downstream applications such as animating static portraits and view-consistent video motion editing.
- MagicAvatar: Multimodal Avatar Generation and Animation24
The flexibility of MagicAvatar is demonstrated through various applications, including text-guided and video-guided avatar generation, as well as multimodal avatar animation, which enables the animation of the provided human identity according to the specific motion derived from the first stage.
- ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance22
ClassDiffusion is presented, a technique that leverages a **semantic preservation loss** to explicitly regulate the concept space when learning a new concept and effectively improves the compositional abilities of fine-tuning models.
- AvatarStudio: High-Fidelity and Animatable 3D Avatar Creation from Text21
Av AvatarStudio is proposed, a generative model that yields explicit textured 3D meshes for animatable human avatars and incorporates articulation modeling into the explicit mesh representation to support high-resolution rendering and avatar animation.
- MagicProp: Diffusion-based Video Editing via Motion-aware Appearance Propagation19
Overall, MagicProp combines the flexibility of image-editing techniques with the superior temporal consistency of autoregressive modeling, enabling flexible editing of object types and aesthetic styles in arbitrary regions of input videos while maintaining good temporal consistency across frames.
- AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models13
A novel method AdjointDPM is proposed, which first generates new samples from diffusion models by solving the corresponding probability-flow ODEs and uses the adjoint sensitivity method to backpropagate the gradients of the loss to the models' parameters by solving another augmented ODE.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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