# Generating Detailed Character Motion from Blocking Poses

arXiv: https://arxiv.org/abs/2509.16064 · HTML: https://mlsanity.com/paper/2509.16064/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 26 of its 40 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Placed in

- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Builds on (most cited first, 26 of 26)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model](https://mlsanity.com/paper/2208.15001/) (2022)
- [PhysDiff: Physics-Guided Human Motion Diffusion Model](https://mlsanity.com/paper/2212.02500/) (2022)
- [Human Motion Diffusion as a Generative Prior](https://mlsanity.com/paper/2303.01418/) (2023)
- [EDGE: Editable Dance Generation From Music](https://mlsanity.com/paper/2211.10658/) (2022)
- [OmniControl: Control Any Joint at Any Time for Human Motion Generation](https://mlsanity.com/paper/2310.08580/) (2023)
- [Robust Motion In-betweening](https://mlsanity.com/paper/2102.04942/) (2021)
- [Guided Motion Diffusion for Controllable Human Motion Synthesis](https://mlsanity.com/paper/2305.12577/) (2023)
- [Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion](https://mlsanity.com/paper/2304.01893/) (2023)
- [Convolutional Autoencoders for Human Motion Infilling](https://mlsanity.com/paper/2010.11531/) (2020)
- [Flexible Motion In-betweening with Diffusion Models](https://mlsanity.com/paper/2405.11126/) (2024)
- [NeMF: Neural Motion Fields for Kinematic Animation](https://mlsanity.com/paper/2206.03287/) (2022)
- [Human-Object Interaction from Human-Level Instructions](https://mlsanity.com/paper/2406.17840/) (2024)
- [MotionFix: Text-Driven 3D Human Motion Editing](https://mlsanity.com/paper/2408.00712/) (2024)
- [Recurrent Transition Networks for Character Locomotion](https://mlsanity.com/paper/1810.02363/) (2018)
- [Iterative Motion Editing with Natural Language](https://mlsanity.com/paper/2312.11538/) (2023)
- [MaskControl: Spatio-Temporal Control for Masked Motion Synthesis](https://mlsanity.com/paper/2410.10780/) (2024)
- [Motion In-Betweening with Phase Manifolds](https://mlsanity.com/paper/2308.12751/) (2023)
- [InsActor: Instruction-driven Physics-based Characters](https://mlsanity.com/paper/2312.17135/) (2023)
- [Generative Tweening: Long-term Inbetweening of 3D Human Motions](https://mlsanity.com/paper/2005.08891/) (2020)
- [Sketch2Anim: Towards Transferring Sketch Storyboards into 3D Animation](https://mlsanity.com/paper/2504.19189/) (2025)
- [Continuous Intermediate Token Learning with Implicit Motion Manifold for Keyframe Based Motion Interpolation](https://mlsanity.com/paper/2303.14926/) (2023)
- [Enhanced Fine-grained Motion Diffusion for Text-driven Human Motion Synthesis](https://mlsanity.com/paper/2305.13773/) (2023)
- [Generative Motion Infilling From Imprecisely Timed Keyframes](https://mlsanity.com/paper/2503.01016/) (2025)

## Abstract (arXiv)

We focus on the problem of using generative diffusion models for the task of motion detailing: converting a rough version of a character animation, represented by a sparse set of coarsely posed, and imprecisely timed blocking poses, into a detailed, natural looking character animation. Current diffusion models can address the problem of correcting the timing of imprecisely timed poses, but we find that no good solution exists for leveraging the diffusion prior to enhance a sparse set of blocking poses with additional pose detail. We overcome this challenge using a simple inference-time trick. At certain diffusion steps, we blend the outputs of an unconditioned diffusion model with input blocking pose constraints using per-blocking-pose tolerance weights, and pass this result in as the input condition to an pre-existing motion retiming model. We find this approach works significantly better than existing approaches that attempt to add detail by blending model outputs or via expressing blocking pose constraints as guidance. The result is the first diffusion model that can robustly convert blocking-level poses into plausible detailed character animations.