Skip to main content

Implementation of D4RT, Efficiently Reconstructing Dynamic Scenes

Project description

d4rt (wip)

Implementation of D4RT, Efficiently Reconstructing Dynamic Scenes, Deepmind

install

$ pip install d4rt

usage

from torch import randn, randint
from d4rt.d4rt import D4RT

model = D4RT(
    dim = 512,
    video_image_size = 128,
    video_patch_size = 32,
    video_max_time_len = 10,
    enc_depth = 6,
    dec_depth = 6
)

videos = randn(2, 10, 3, 128, 128)

video_lens = randint(2, 10, (2,)) # handle variable lengthed video, can be None for max length always

# inputs

coors = randint(0, 128, (2, 5, 2))
time_src = randint(0, 10, (2, 5))
time_tgt = randint(0, 10, (2, 5))
time_camera = randint(0, 10, (2, 5))

query_lens = randint(1, 5, (2,)) # handle varaible lengthed queries

# output

points = randn(2, 5, 3)

loss = model(
    videos,
    video_lens = video_lens,
    coors = coors,
    time_src = time_src,
    time_tgt = time_tgt,
    time_camera = time_camera,
    query_lens = query_lens,
    points = points,
)

loss.backward()

# without giving the output, it returns the prediction

pred = model(
    videos,
    coors = coors,
    time_src = time_src,
    time_tgt = time_tgt,
    time_camera = time_camera
)

assert pred.shape == (2, 5, 3)

citations

@article{zhang2025d4rt,
    title   = {Efficiently Reconstructing Dynamic Scenes One D4RT at a Time},
    author  = {Zhang, Chuhan and Le Moing, Guillaume and Koppula, Skanda and Rocco, Ignacio and Momeni, Liliane and Xie, Junyu and Sun, Shuyang and Sukthankar, Rahul and Barral, Jo{\"e}lle K. and Hadsell, Raia and Ghahramani, Zoubin and Zisserman, Andrew and Zhang, Junlin and Sajjadi, Mehdi S. M.},
    journal = {arXiv preprint},
    year    = {2025}
}
@inproceedings{liu2026geometryaware,
    title   = {Geometry-aware 4D Video Generation for Robot Manipulation},
    author  = {Zeyi Liu and Shuang Li and Eric Cousineau and Siyuan Feng and Benjamin Burchfiel and Shuran Song},
    booktitle = {The Fourteenth International Conference on Learning Representations},
    year    = {2026},
    url     = {https://openreview.net/forum?id=18gC6pZVVc}
}
@misc{joseph2026interpretingphysicsvideoworld,
    title   = {Interpreting Physics in Video World Models},
    author  = {Sonia Joseph and Quentin Garrido and Randall Balestriero and Matthew Kowal and Thomas Fel and Shahab Bakhtiari and Blake Richards and Mike Rabbat},
    year    = {2026},
    eprint  = {2602.07050},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV},
    url={https://arxiv.org/abs/2602.07050},
}
@misc{li2025basicsletdenoisinggenerative,
    title   = {Back to Basics: Let Denoising Generative Models Denoise},
    author  = {Tianhong Li and Kaiming He},
    year    = {2025},
    eprint  = {2511.13720},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV},
    url     = {https://arxiv.org/abs/2511.13720},
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

d4rt-0.0.7.tar.gz (8.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

d4rt-0.0.7-py3-none-any.whl (7.5 kB view details)

Uploaded Python 3

File details

Details for the file d4rt-0.0.7.tar.gz.

File metadata

  • Download URL: d4rt-0.0.7.tar.gz
  • Upload date:
  • Size: 8.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.8.13

File hashes

Hashes for d4rt-0.0.7.tar.gz
Algorithm Hash digest
SHA256 5655040a7e50833af654ea0b7c3ba7b1ef6bd56aeb5d969d2ece4927519f3de4
MD5 dfc0c151f61a0c34af7dd7683990090d
BLAKE2b-256 cb99d31729be80ad720b8f328775afa9a507859ee88e5b5b3be64afe46f1de0c

See more details on using hashes here.

File details

Details for the file d4rt-0.0.7-py3-none-any.whl.

File metadata

  • Download URL: d4rt-0.0.7-py3-none-any.whl
  • Upload date:
  • Size: 7.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.8.13

File hashes

Hashes for d4rt-0.0.7-py3-none-any.whl
Algorithm Hash digest
SHA256 806d3da6e93d83899620dfa801e804c8e7b29cc2cd69f61bbc358cd1e4ba250e
MD5 f6751ed47dd9344cb98f20f89fafc863
BLAKE2b-256 8cf87ab1dd3ba130f5162b7025998ea08b5e32510f2443fefbab832b137b191a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page