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Found 8 papers, 4 papers with code
AMD:Anatomical Motion Diffusion with Interpretable Motion Decomposition and Fusion
Generating realistic human motion sequences from text descriptions is a challenging task that requires capturing the rich expressiveness of both natural language and human motion. Recent advances in diffusion models have enabled significant progress in human motion synthesis. However, existing methods struggle to handle text inputs that describe complex or long motions. In this paper, we propose the Adaptable Motion Diffusion (AMD) model, which leverages a Large Language Model (LLM) to parse the input text into a sequence of concise and interpretable anatomical scripts that correspond to the target motion. This process exploits the LLM's ability to provide anatomical guidance for complex motion synthesis. We then devise a two-branch fusion scheme that balances the influence of the input text and the anatomical scripts on the inverse diffusion process, which adaptively ensures the semantic fidelity and diversity of the synthesized motion. Our method can effectively handle texts with complex or long motion descriptions, where existing methods often fail.
Optimized View and Geometry Distillation from Multi-view Diffuser
We leverage the rendered views from the optimized radiance field as the basis and develop a two-step specialization process of a 2D diffusion model, which is adept at conducting object-specific denoising and generating high-quality multi-view images.
Fine-grained Appearance Transfer with Diffusion Models
A pivotal aspect of our approach is the strategic use of the predicted $x_0$ space by diffusion models within the latent space of diffusion processes.
Progressive Text-to-Image Diffusion with Soft Latent Direction
In spite of the rapidly evolving landscape of text-to-image generation, the synthesis and manipulation of multiple entities while adhering to specific relational constraints pose enduring challenges.
SUGAR: Spherical Ultrafast Graph Attention Framework for Cortical Surface Registration
Cortical surface registration plays a crucial role in aligning cortical functional and anatomical features across individuals.
NeMF: Inverse Volume Rendering with Neural Microflake Field
Recovering the physical attributes of an object's appearance from its images captured under an unknown illumination is challenging yet essential for photo-realistic rendering.
Dynamic Feature Pruning and Consolidation for Occluded Person Re-Identification
The framework mainly consists of a sparse encoder, a multi-view feature mathcing module, and a feature consolidation decoder.
Spatio-channel Attention Blocks for Cross-modal Crowd Counting
Crowd counting research has made significant advancements in real-world applications, but it remains a formidable challenge in cross-modal settings.
