What is MotionFix
MotionFix is an open-source 3D human motion editing project that achieves precise editing of 3D human motion by combining natural language descriptions and the conditional diffusion model TMED. A triplet dataset containing source motion, target motion, and edit text was constructed in a semi-automatic way, solving the problem of insufficient training data. The TMED model can understand the edit instructions in the text description, apply them to the source motion, and generate modified motion that matches the description while maintaining coherence with the source motion. It has broad application potential in animation production, virtual reality, game development, and other fields, providing a new text-based control and editing method for 3D human motion editing.
Main features of MotionFix
- Text-driven motion editing: Supports users in guiding and editing 3D human motion through natural language descriptions, enabling precise adjustment of motion details.
- Dataset construction: A triplet dataset containing source motion, target motion, and edit text was constructed through a semi-automatic method, providing data support for model training.
- Conditional diffusion model (TMED): Based on a conditional diffusion model, it gradually and iteratively generates edited motion according to the source motion and text instructions, maintaining motion coherence and accuracy.
- Multimodal input processing: The TMED model can process multiple input modalities including text descriptions, source motion, and noise vectors, improving the flexibility and adaptability of editing.
- Editing guidance and control: Controls the degree of influence of the source motion and text instructions during the generation process through the guidance scale, supporting users in adjusting the similarity between the generated motion and the source motion as well as adherence to the text description.
- Innovative evaluation metrics: Introduces retrieval-based evaluation metrics to quantitatively assess the accuracy of the generated motion and its closeness to the source motion, providing objective evaluation of motion editing performance.
Technical principles of MotionFix
- Semi-automatic data collection: Based on existing motion capture datasets and text-to-motion retrieval models (such as TMR), similar motion pairs are automatically found, and the dataset is constructed by manually annotating edit text.
- SMPL motion representation: Uses the SMPL (Skinned Multi-Person Linear) model to parameterize human motion, a parametric representation method widely used in 3D human modeling and motion capture.
- Encoder-decoder architecture: The TMED model includes multiple encoders to process timestep, text, and motion inputs, as well as a Transformer model to process the encoding of all inputs.
- Classifier-Free Guidance: During the sampling process, classifier-free guidance is used to adjust the influence of the source motion and text conditions, thereby controlling the generation process.
- Retrieval baseline: To evaluate the quality of generated motion, retrieval-based metrics such as generation-to-target retrieval and generation-to-source retrieval are used to measure the similarity of the generated motion to the target or source motion.
MotionFix project address
- Project official website：motionfix.is.tue.mpg.de
- Github repository：https://github.com/atnikos/motionfix
- arXiv technical paper：https://www.arxiv.org/pdf/2408.00712
- Online experience Demo：https://huggingface.co/spaces/atnikos/motionfix-demo
Application scenarios of MotionFix
- Animation production and editing: MotionFix can be used in animation production workflows, supporting animators to edit and adjust 3D human motions through text descriptions to match specific animation needs.
- Game Development: In game development, MotionFix can be used to generate and edit the motions of characters in games, making the motions more aligned with game design needs and improving the realism and diversity of motions.
- Virtual Reality (VR) and Augmented Reality (AR): In VR and AR applications, MotionFix can be used to create more natural and realistic human motions, enhancing users' immersion and interactive experience.
- Motion Analysis and Training: MotionFix can be used in sports training and motion analysis, helping improve technique or prevent sports injuries by editing and adjusting athletes' motions.