Researchers have developed new methods for generating realistic human motion that accurately follows specified trajectories and textual descriptions. One approach, CMC, uses a two-stage diffusion process to first ensure trajectory adherence and then complete the full-body motion, incorporating a selective inpainting mechanism to improve training. Another method, MSCoT, employs a multi-scale, coarse-to-fine strategy with efficient token guidance and a refinement module for faster, more precise control. A third framework, AnchorRoute, uses sparse anchors as a scaffold for both generation and refinement, integrating a diffusion prior with a residual-based refinement solver to enhance control accuracy while maintaining motion quality. AI
影响 Advances in controllable human motion synthesis could significantly impact animation, gaming, and robotics by enabling more realistic and interactive character behaviors.
排序理由 Multiple research papers introduce novel methods for human motion generation with trajectory control.
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