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We introduce Multi-view Ancestral Sampling (MAS), a method for 3D motiongeneration, using 2D diffusion models that were trained on motions obtainedfrom in-the-wild videos. As such, MAS opens opportunities to exciting anddiverse fields of motion previously under-explored as 3D data is scarce andhard to collect. MAS works by simultaneously denoising multiple 2D motionsequences representing different views of the same 3D motion. It ensuresconsistency across all views at each diffusion step by combining the individualgenerations into a unified 3D sequence, and projecting it back to the originalviews. We demonstrate MAS on 2D pose data acquired from videos depictingprofessional basketball maneuvers, rhythmic gymnastic performances featuring aball apparatus, and horse races. In each of these domains, 3D motion capture isarduous, and yet, MAS generates diverse and realistic 3D sequences. Unlike theScore Distillation approach, which optimizes each sample by repeatedly applyingsmall fixes, our method uses a sampling process that was constructed for thediffusion framework. As we demonstrate, MAS avoids common issues such asout-of-domain sampling and mode-collapse. https://guytevet.github.io/mas-page/

MAS: Multi-view Ancestral Sampling for 3D motion generation using 2D diffusion