• parent: domain

• GenMM Example-basedMotion Synthesis via Generative Motion Matching

  • training free, generated in a fraction of a second

• DiffMimic EfficientMotion Mimicking with Differentiable Physics

• Physics-basedMotion Retargeting from Sparse Inputs

  • from sparse human sensor data to characters of various morphologies

• TC4D Trajectory-Conditioned Text-to-4D Generation

  • factors motion into global and local components

  • synthesis of scenes animated along arbitrary trajectories

PAE - PHASE

• PAEpaperdetails DeepPhase: Periodic Autoencoders for Learning Motion Phase Manifolds

• decompose movement in composable phase skeletons

DIFFUSION MOTION

• parent: diffusionINTERACTIONS

• ReMoDiffuse Retrieval-AugmentedMotion DiffusionModel

  • semantic augmented database, + previous motion features sequences

• MotionDiffuser Controllable Multi-Agent Motion Prediction using Diffusion (future trajectories over multiple agents)

• TEDi Temporally-Entangled Diffusion for Long-Term Motion Synthesis

  • adapt the gradual diffusion concept (along a diffusion time-axis) into temporal-axis of the motion sequence

  • extend DDPM framework to temporally varying denoising, thereby entangling the two axes; long-term motion

• GeneratingFine-Grained Human Motions Using ChatGPT-Refined Descriptions

  • FG-MDM: Fine-Grained Human Motion Diffusion Model

• DreaMo Articulated3D Reconstruction From A Single Casual Video ==best==

  • diffusion model to hallucinate invisible parts and to enhance the geometry

• CAGE Controllable Articulation GEneration

  • attention modules designed to extract correlations between part attributes, connectivity graph as input

HUMAN

• MoMask Generative MaskedModeling of 3D Human Motions

  • progressively predict the next-layer tokens based on the results from current layer

  • hierarchical quantization scheme is employed to represent human motion as motion tokens

• TowardsDetailed Text-to-Motion Synthesis via Basic-to-Advanced Hierarchical Diffusion Model

  • low-dimensional latent consistent promt, while high-dimensional follows detail-enhancing process

• RealisticHuman Motion Generation with Cross-Diffusion Models

  • model reverses either 2D or 3D noise into clean motion during training (2D motion data)

• HuTuMotion Human-Tuned Navigation of Latent Motion Diffusion Models with Minimal Feedback

  • personalized and style-aware human motion generation

• MotionMix Weakly-Supervised Diffusion for Controllable Motion Generation

  • unannotated motions

• GenerativeHuman Motion Stylization in Latent Space

• Self-CorrectingSelf-Consuming Loops For Generative Model Training [training]

  • successfully avoid collapse (of nullifying opposite movements)

TIMELINE

• Multi-TrackTimeline Control for Text-Driven 3D Human Motion Generation ==best==

  • using a single text prompt as input lacks the fine-grained control needed by animators

  • multi-track timeline of multiple prompts organized in temporal intervals that may overlap

• SeamlessHuman Motion Composition with Blended Positional Encodings ==best==

  • diffusion-based model for seamless human motion compositions

  • global coherence at absolute stage, smooth transitions at relative stage

• Large MotionModel for Unified Multi-Modal Motion Generation

  • multimodal input: text, speech, music, video

EDIT

• DNO Optimizing Diffusion Noise Can Serve As Universal Motion Priors

  • preserve while accommodating editing modes: changing trajectory, pose, joint location, obstacles