Go to Zero: Towards Zero-shot Motion Generation with Million-scale Data
Generating diverse and natural human motion sequences based on textual
descriptions constitutes a fundamental and challenging research area within the
domains of computer vision, graphics, and robotics. Despite significant
advancements in this field, current methodologies often face challenges
regarding zero-shot generalization capabilities, largely attributable to the
limited size of training datasets. Moreover, the lack of a comprehensive
evaluation framework impedes the advancement of this task by failing to
identify directions for improvement. In this work, we aim to push
text-to-motion into a new era, that is, to achieve the generalization ability
of zero-shot. To this end, firstly, we develop an efficient annotation pipeline
and introduce MotionMillion-the largest human motion dataset to date, featuring
over 2,000 hours and 2 million high-quality motion sequences. Additionally, we
propose MotionMillion-Eval, the most comprehensive benchmark for evaluating
zero-shot motion generation. Leveraging a scalable architecture, we scale our
model to 7B parameters and validate its performance on MotionMillion-Eval. Our
results demonstrate strong generalization to out-of-domain and complex
compositional motions, marking a significant step toward zero-shot human motion
generation. The code is available at
https://github.com/VankouF/MotionMillion-Codes.
686f2579d938c25d68441b43
Ke Fan
686f2579d938c25d68441b44
Shunlin Lu
686f2579d938c25d68441b45
Minyue Dai
claimed_verified
2025-07-10T07:09:19.845Z
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Minyue Dai
user
Jixi111
686f2579d938c25d68441b46
Runyi Yu
686f2579d938c25d68441b47
Lixing Xiao
686f2579d938c25d68441b48
Zhiyang Dou
claimed_verified
2025-07-17T08:21:12.199Z
645223fb01d7bd9555ea399a
https://cdn-avatars.huggingface.co/v1/production/uploads/645223fb01d7bd9555ea399a/fVR7XmGg6pMSRKx9sEdvT.png
Zhiyang Dou
user
frankzydou
686f2579d938c25d68441b49
Junting Dong
686f2579d938c25d68441b4a
Lizhuang Ma
686f2579d938c25d68441b4b
Jingbo Wang
Go to Zero: Towards Zero-shot Motion Generation with Million-scale Data
Generating diverse and natural human motion sequences based on textual
descriptions constitutes a fundamental and challenging research area within the
domains of computer vision, graphics, and robotics. Despite significant
advancements in this field, current methodologies often face challenges
regarding zero-shot generalization capabilities, largely attributable to the
limited size of training datasets. Moreover, the lack of a comprehensive
evaluation framework impedes the advancement of this task by failing to
identify directions for improvement. In this work, we aim to push
text-to-motion into a new era, that is, to achieve the generalization ability
of zero-shot. To this end, firstly, we develop an efficient annotation pipeline
and introduce MotionMillion-the largest human motion dataset to date, featuring
over 2,000 hours and 2 million high-quality motion sequences. Additionally, we
propose MotionMillion-Eval, the most comprehensive benchmark for evaluating
zero-shot motion generation. Leveraging a scalable architecture, we scale our
model to 7B parameters and validate its performance on MotionMillion-Eval. Our
results demonstrate strong generalization to out-of-domain and complex
compositional motions, marking a significant step toward zero-shot human motion
generation. The code is available at
https://github.com/VankouF/MotionMillion-Codes.
A new dataset and evaluation framework for text-to-motion generation achieve zero-shot generalization using a large-scale model.
MotionMillion MotionMillion-Eval scalable architecture