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

arXiv: https://arxiv.org/abs/2310.14729 · HTML: https://mlsanity.com/paper/2310.14729/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 42 of its 61 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 30 of 42)

- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Zero-1-to-3: Zero-shot One Image to 3D Object](https://mlsanity.com/paper/2303.11328/) (2023)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [Improved Techniques for Training Score-Based Generative Models](https://mlsanity.com/paper/2006.09011/) (2020)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [MVDream: Multi-view Diffusion for 3D Generation](https://mlsanity.com/paper/2308.16512/) (2023)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model](https://mlsanity.com/paper/2208.15001/) (2022)
- [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation](https://mlsanity.com/paper/2212.00774/) (2022)
- [TEMOS: Generating diverse human motions from textual descriptions](https://mlsanity.com/paper/2204.14109/) (2022)
- [Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions](https://mlsanity.com/paper/2303.12789/) (2023)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [Action2Motion: Conditioned Generation of 3D Human Motions](https://mlsanity.com/paper/2007.15240/) (2020)
- [MotionCLIP: Exposing Human Motion Generation to CLIP Space](https://mlsanity.com/paper/2203.08063/) (2022)
- [Human Motion Diffusion as a Generative Prior](https://mlsanity.com/paper/2303.01418/) (2023)
- [The KIT Motion-Language Dataset](https://mlsanity.com/paper/1607.03827/) (2016)
- [Language2Pose: Natural Language Grounded Pose Forecasting](https://mlsanity.com/paper/1907.01108/) (2019)
- [MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis](https://mlsanity.com/paper/2212.04495/) (2022)
- [FLAME: Free-form Language-based Motion Synthesis & Editing](https://mlsanity.com/paper/2209.00349/) (2022)
- [AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time](https://mlsanity.com/paper/2211.03375/) (2022)
- [MotionBERT: A Unified Perspective on Learning Human Motion Representations](https://mlsanity.com/paper/2210.06551/) (2022)
- [Tracking Everything Everywhere All at Once](https://mlsanity.com/paper/2306.05422/) (2023)
- [Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation](https://mlsanity.com/paper/2303.07937/) (2023)
- [Delta Denoising Score](https://mlsanity.com/paper/2304.07090/) (2023)
- [MagicPony: Learning Articulated 3D Animals in the Wild](https://mlsanity.com/paper/2211.12497/) (2022)
- [HumanNorm: Learning Normal Diffusion Model for High-quality and Realistic 3D Human Generation](https://mlsanity.com/paper/2310.01406/) (2023)

## Built on by (most cited first, 30 of 37)

- [Collaborative Video Diffusion: Consistent Multi-video Generation with Camera Control](https://mlsanity.com/paper/2405.17414/) (2024)
- [CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character control](https://mlsanity.com/paper/2410.03441/) (2024)
- [Rethinking Diffusion for Text-Driven Human Motion Generation: Redundant Representations, Evaluation, and Masked Autoregression](https://mlsanity.com/paper/2411.16575/) (2024)
- [DartControl: A Diffusion-Based Autoregressive Motion Model for Real-Time Text-Driven Motion Control](https://mlsanity.com/paper/2410.05260/) (2024)
- [PhysHOI: Physics-Based Imitation of Dynamic Human-Object Interaction](https://mlsanity.com/paper/2312.04393/) (2023)
- [Flexible Motion In-betweening with Diffusion Models](https://mlsanity.com/paper/2405.11126/) (2024)
- [AnyTop: Character Animation Diffusion with Any Topology](https://mlsanity.com/paper/2502.17327/) (2025)
- [Monkey See, Monkey Do: Harnessing Self-attention in Motion Diffusion for Zero-shot Motion Transfer](https://mlsanity.com/paper/2406.06508/) (2024)
- [Absolute Coordinates Make Motion Generation Easy](https://mlsanity.com/paper/2505.19377/) (2025)
- [AvatarGO: Zero-shot 4D Human-Object Interaction Generation and Animation](https://mlsanity.com/paper/2410.07164/) (2024)
- [Ponymation: Learning Articulated 3D Animal Motions from Unlabeled Online Videos](https://mlsanity.com/paper/2312.13604/) (2023)
- [Consolidating Attention Features for Multi-view Image Editing](https://mlsanity.com/paper/2402.14792/) (2024)
- [Pay Attention and Move Better: Harnessing Attention for Interactive Motion Generation and Training-free Editing](https://mlsanity.com/paper/2410.18977/) (2024)
- [Sketch2Anim: Towards Transferring Sketch Storyboards into 3D Animation](https://mlsanity.com/paper/2504.19189/) (2025)
- [Dance Like a Chicken: Low-Rank Stylization for Human Motion Diffusion](https://mlsanity.com/paper/2503.19557/) (2025)
- [AnimaX: Animating the Inanimate in 3D with Joint Video-Pose Diffusion Models](https://mlsanity.com/paper/2506.19851/) (2025)
- [Lifting Motion to the 3D World via 2D Diffusion](https://mlsanity.com/paper/2411.18808/) (2024)
- [Motion-2-To-3: Leveraging 2D Motion Data for 3D Motion Generations](https://mlsanity.com/paper/2412.13111/) (2024)
- [DIMO: Diverse 3D Motion Generation for Arbitrary Objects](https://mlsanity.com/paper/2511.07409/) (2025)
- [How to Move Your Dragon: Text-to-Motion Synthesis for Large-Vocabulary Objects](https://mlsanity.com/paper/2503.04257/) (2025)
- [MotionVerse: A Unified Multimodal Framework for Motion Comprehension, Generation and Editing](https://mlsanity.com/paper/2509.23635/) (2025)
- [CryoFM: A Flow-based Foundation Model for Cryo-EM Densities](https://mlsanity.com/paper/2410.08631/) (2024)
- [Generative Motion Infilling From Imprecisely Timed Keyframes](https://mlsanity.com/paper/2503.01016/) (2025)
- [Learning to Control Physically-simulated 3D Characters via Generating and Mimicking 2D Motions](https://mlsanity.com/paper/2512.08500/) (2025)
- [AnyLift: Scaling Motion Reconstruction from Internet Videos via 2D Diffusion](https://mlsanity.com/paper/2604.17818/) (2026)
- [PoseBH: Prototypical Multi-Dataset Training Beyond Human Pose Estimation](https://mlsanity.com/paper/2505.17475/) (2025)
- [Bimanual 3D Hand Motion and Articulation Forecasting in Everyday Images](https://mlsanity.com/paper/2510.06145/) (2025)
- [Learning Context-Adaptive Motion Priors for Masked Motion Diffusion Models with Efficient Kinematic Attention Aggregation](https://mlsanity.com/paper/2603.07697/) (2026)
- [Self-Intersection-Aware 3D Human Motion Generation Using an Efficient Human Sphere Proxy](https://mlsanity.com/paper/2605.26744/) (2026)
- [MotionDreamer: Exploring Semantic Video Diffusion features for Zero-Shot 3D Mesh Animation](https://mlsanity.com/paper/2405.20155/) (2024)

## Abstract (arXiv)

We introduce Multi-view Ancestral Sampling (MAS), a method for 3D motion generation, using 2D diffusion models that were trained on motions obtained from in-the-wild videos. As such, MAS opens opportunities to exciting and diverse fields of motion previously under-explored as 3D data is scarce and hard to collect. MAS works by simultaneously denoising multiple 2D motion sequences representing different views of the same 3D motion. It ensures consistency across all views at each diffusion step by combining the individual generations into a unified 3D sequence, and projecting it back to the original views. We demonstrate MAS on 2D pose data acquired from videos depicting professional basketball maneuvers, rhythmic gymnastic performances featuring a ball apparatus, and horse races. In each of these domains, 3D motion capture is arduous, and yet, MAS generates diverse and realistic 3D sequences. Unlike the Score Distillation approach, which optimizes each sample by repeatedly applying small fixes, our method uses a sampling process that was constructed for the diffusion framework. As we demonstrate, MAS avoids common issues such as out-of-domain sampling and mode-collapse. https://guytevet.github.io/mas-page/