Virtual avatar generation models as world navigators
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Abstract (arXiv)
We introduce SABR-CLIMB, a novel video model simulating human movement in rock climbing environments using a virtual avatar. Our diffusion transformer predicts the sample instead of noise in each diffusion step and ingests entire videos to output complete motion sequences. By leveraging a large proprietary dataset, NAV-22M, and substantial computational resources, we showcase a proof of concept for a system to train general-purpose virtual avatars for complex tasks in robotics, sports, and healthcare.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 10 references that are in the map. First posted on arXiv 3 Jun 2024.
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References · 8
- 2017Attention Is All You Needcited by 60,361
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
- 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,430
- 2021Palette: Image-to-Image Diffusion Modelscited by 1,300
- 2023MotionGPT: Human Motion as a Foreign Languagecited by 527
- 2024MoST: Multi-modality Scene Tokenization for Motion Predictioncited by 8
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- 2024VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis
- 2024Make-Your-Anchor: A Diffusion-based 2D Avatar Generation Framework
- 2024AMG: Avatar Motion Guided Video Generation
- 2025Hyper Diffusion Avatars: Dynamic Human Avatar Generation using Network Weight Space Diffusion
- 2024CyberHost: Taming Audio-driven Avatar Diffusion Model with Region Codebook Attention
- 2024Report on Methods and Applications for Crafting 3D Humans ↗
- 2023Dancing Avatar: Pose and Text-Guided Human Motion Videos Synthesis with Image Diffusion Model
- 2025MVP4D: Multi-View Portrait Video Diffusion for Animatable 4D Avatars