[CVPR 2024 (Highlight)] Relightable and Animatable Neural Avatar from Sparse-View Video

Paper | Project Page | Video

This paper tackles the challenge of creating relightable and animatable neural avatars from sparse-view (or even monocular) videos of dynamic humans under unknown illumination. Compared to studio environments, this setting is more practical and accessible but poses an extremely challenging ill-posed problem.

Quick Start

Prepare Trained Model

We provide an example trained model for the xuzhen sequence of the MobileStage dataset:

The base AniSDF model can be downloaded from here: anisdf.zip.
The RelightableAvatar model can be downloaded from here: relightable.zip.
Furthermore, you'll need to download a skeleton dataset (very small, only with some basic information needed to run relightable_avatar) here: minimal.tar.gz.
- The skeleton dataset is only required if the full dataset hasn't been downloaded and placed at its corresponding location.
For relighting, we also provide the downscaled environment map: 16x32.zip. If you see errors about data/lighting, download this.

Trained model and skeleton data placement:

The base AniSDF model should be put in data/trained_model/deform/xuzhen_12v_geo, after which we expect latest.pth to reside at data/trained_model/deform/xuzhen_12v_geo/latest.pth.
The RelightableAvatar model should be put in data/trained_model/relight/xuzhen_12v_geo_fix_mat, after which we expect latest.pth to reside at data/trained_model/deform/xuzhen_12v_geo_fix_mat/latest.pth.
The skeleton dataset should be extracted at data/mobile_stage/xuzhen, leading to data/mobile_stage/xuzhen/motion.npz....
The environment map should be placed at data/lighting, after which a data/lighting/16x32 folder is expected.

Prepare Custom Pose

For the human pose, we use a compact motion.npz to store the pose, shape and global translation parameters. You can find an example file at data/mobile_stage/xuzhen/motion.npz. If you've downloaded the skeleton data provided above, you should also see other motion files ending with .npz.

We also provide a script for preparing other common motion formats into our motion.npz structure at scripts/toosl/prepare_motion.py. You can learn more about the structure of motion.npz in this script.

Run the AniSDF Model With Custom Pose

# Fixed view + novel pose
python run.py -t visualize -c configs/mobile_stage/xuzhen_12v_geo.yaml ground_attach_envmap False vis_pose_sequence True num_eval_frame 100 H 512 W 512 novel_view_ixt_ratio 0.80 vis_ext .png test_view 0, test_motion gPO_sFM_cAll_d12_mPO1_ch16.npz

# Novel rotating view + novel pose
python run.py -t visualize -c configs/mobile_stage/xuzhen_12v_geo.yaml ground_attach_envmap False vis_novel_view True perform True num_render_view 100 H 512 W 512 novel_view_ixt_ratio 0.80 vis_ext .png test_motion gPO_sFM_cAll_d12_mPO1_ch16.npz

# For faster rendering, use sphere tracing instead of volume rendering by adding the `vis_sphere_tracing True` entry
# Will speed up the rendering, but might produce artifacts

Try to tune these entries H 512 W 512 novel_view_ixt_ratio 0.80 to customize your output image. Moreover, select the source view using test_view 0, and the motion using test_motion gPO_sFM_cAll_d12_mPO1_ch16.npz. num_eval_frame and num_render_view control the number of rendered images for the novel pose and novel view setting, respectively.

Example motions files are provided at data/mobile_stage/xuzhen/*.npz. To use skeleton data, customize your dataset root using test_dataset.data_root <CUSTOM_DATASET_PATH>. The recommended way of switching to another set of motions is to put the prepared motion file into <CUSTOM_DATASET_PATH> (wherever the test_dataset.data_root points to) and set test_motion. You can also use test_motion to specify a motion file outside the dataset root by providing an absolute path to the motion file.

Run the Relightable Model With Custom Pose

python run.py -t visualize -c configs/mobile_stage/xuzhen_12v_geo.yaml relighting True vis_novel_light True vis_pose_sequence True vis_rendering_map True vis_shading_map True vis_albedo_map True vis_normal_map True vis_envmap_map True vis_roughness_map True vis_specular_map True vis_surface_map True vis_residual_map True vis_depth_map True num_eval_frame 100 H 512 W 512 novel_view_ixt_ratio 0.80 vis_ext .png vis_ground_shading True test_light '["main", "venetian_crossroads", "pink_sunrise", "shanghai_bund", "venice_sunrise", "quattro_canti", "olat0002-0027", "olat0004-0019"]' test_view 0, extra_prefix "gPO_sFM_cAll_d12_mPO1_ch16" test_motion gPO_sFM_cAll_d12_mPO1_ch16.npz

Todo

Add documentation on training on the SyntheticHuman++ dataset
Add documentation on training on the MobileStage dataset

Citation

If you find this code useful for your research, please cite us using the following BibTeX entry.

@inproceedings{xu2024relightable,
    title={Relightable and Animatable Neural Avatar from Sparse-View Video},
    author={Xu, Zhen and Peng, Sida and Geng, Chen and Mou, Linzhan and Yan, Zihan and Sun, Jiaming and Bao, Hujun and Zhou, Xiaowei},
    booktitle={CVPR},
    year={2024}
}

zju3dv/RelightableAvatar