# GAC-GAN: A General Method for Appearance-Controllable Human Video Motion Transfer

arXiv: https://arxiv.org/abs/1911.10672 · HTML: https://mlsanity.com/paper/1911.10672/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 14 of 14)

- [MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion](https://mlsanity.com/paper/2311.12052/) (2023)
- [Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production](https://mlsanity.com/paper/2203.15354/) (2022)
- [SignDiff: Diffusion Model for American Sign Language Production](https://mlsanity.com/paper/2308.16082/) (2023)
- [GUAVA: Generalizable Upper Body 3D Gaussian Avatar](https://mlsanity.com/paper/2505.03351/) (2025)
- [A Feature-space Multimodal Data Augmentation Technique for Text-video Retrieval](https://mlsanity.com/paper/2208.02080/) (2022)
- [Flow Guided Transformable Bottleneck Networks for Motion Retargeting](https://mlsanity.com/paper/2106.07771/) (2021)
- [FDA-GAN: Flow-based Dual Attention GAN for Human Pose Transfer](https://mlsanity.com/paper/2112.00281/) (2021)
- [Delving into the Frequency: Temporally Consistent Human Motion Transfer in the Fourier Space](https://mlsanity.com/paper/2209.00233/) (2022)
- [AnimateAnywhere: Rouse the Background in Human Image Animation](https://mlsanity.com/paper/2504.19834/) (2025)
- [A Reconfigurable Convolution-in-Pixel CMOS Image Sensor Architecture](https://mlsanity.com/paper/2101.03308/) (2021)
- [SignLLM: Sign Language Production Large Language Models](https://mlsanity.com/paper/2405.10718/) (2024)
- [One-shot Human Motion Transfer via Occlusion-Robust Flow Prediction and Neural Texturing](https://mlsanity.com/paper/2412.06174/) (2024)
- [GLocal: Global Graph Reasoning and Local Structure Transfer for Person Image Generation](https://mlsanity.com/paper/2112.00263/) (2021)
- [EMOSH: Expressive Motion and Shape Disentanglement for Human Animation](https://mlsanity.com/paper/2606.28026/) (2026)

## Abstract (arXiv)

Human video motion transfer has a wide range of applications in multimedia, computer vision and graphics. Recently, due to the rapid development of Generative Adversarial Networks (GANs), there has been significant progress in the field. However, almost all existing GAN-based works are prone to address the mapping from human motions to video scenes, with scene appearances are encoded individually in the trained models. Therefore, each trained model can only generate videos with a specific scene appearance, new models are required to be trained to generate new appearances. Besides, existing works lack the capability of appearance control. For example, users have to provide video records of wearing new clothes or performing in new backgrounds to enable clothes or background changing in their synthetic videos, which greatly limits the application flexibility. In this paper, we propose GAC-GAN, a general method for appearance-controllable human video motion transfer. To enable general-purpose appearance synthesis, we propose to include appearance information in the conditioning inputs. Thus, once trained, our model can generate new appearances by altering the input appearance information. To achieve appearance control, we first obtain the appearance-controllable conditioning inputs and then utilize a two-stage GAC-GAN to generate the corresponding appearance-controllable outputs, where we utilize an ACGAN loss and a shadow extraction module for output foreground and background appearance control respectively. We further build a solo dance dataset containing a large number of dance videos for training and evaluation. Experimental results show that, our proposed GAC-GAN can not only support appearance-controllable human video motion transfer but also achieve higher video quality than state-of-art methods.