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Found 8 papers, 5 papers with code
EfficientGS: Streamlining Gaussian Splatting for Large-Scale High-Resolution Scene Representation
In the domain of 3D scene representation, 3D Gaussian Splatting (3DGS) has emerged as a pivotal technology.
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.
DiffusionTrack: Diffusion Model For Multi-Object Tracking
In inference, the model refines a set of paired randomly generated boxes to the detection and tracking results in a flexible one-step or multi-step denoising diffusion process.
Compact Transformer Tracker with Correlative Masked Modeling
Transformer framework has been showing superior performances in visual object tracking for its great strength in information aggregation across the template and search image with the well-known attention mechanism.
Transformer Tracking with Cyclic Shifting Window Attention
Transformer architecture has been showing its great strength in visual object tracking, for its effective attention mechanism.
