MotionFix: Text-Driven 3D Human Motion Editing
DescriptionThe focus of this paper is 3D motion editing. Given a 3D human motion
and a textual description of the desired modification, our goal is to generate
an edited motion as described by the text. The challenges include the lack
of training data and the design of a model that faithfully edits the source
motion. In this paper, we address both these challenges. We build a methodology
to semi-automatically collect a dataset of triplets in the form of (i) a
source motion, (ii) a target motion, and (iii) an edit text, and create the new
MotionFix dataset. Having access to such data allows us to train a conditional
diffusion model, TMED, that takes both the source motion and the edit text
as input. We further build various baselines trained only on text-motion
pairs datasets, and show superior performance of our model trained on
triplets. We introduce new retrieval-based metrics for motion editing, and
establish a new benchmark on the evaluation set of MotionFix. Our results
are encouraging, paving the way for further research on fine-grained motion
generation. Code and models will be made publicly available.
and a textual description of the desired modification, our goal is to generate
an edited motion as described by the text. The challenges include the lack
of training data and the design of a model that faithfully edits the source
motion. In this paper, we address both these challenges. We build a methodology
to semi-automatically collect a dataset of triplets in the form of (i) a
source motion, (ii) a target motion, and (iii) an edit text, and create the new
MotionFix dataset. Having access to such data allows us to train a conditional
diffusion model, TMED, that takes both the source motion and the edit text
as input. We further build various baselines trained only on text-motion
pairs datasets, and show superior performance of our model trained on
triplets. We introduce new retrieval-based metrics for motion editing, and
establish a new benchmark on the evaluation set of MotionFix. Our results
are encouraging, paving the way for further research on fine-grained motion
generation. Code and models will be made publicly available.
Event Type
Technical Papers
TimeTuesday, 3 December 20249:00am - 12:00pm JST
LocationHall C, C Block, Level 4
