TPA-Net: Generate A Dataset for Text to Physics-based Animation Article Swipe
Yuxing Qiu
,
Feng Gao
,
Minchen Li
,
Govind Thattai
,
Yin Yang
,
Chenfanfu Jiang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.13887
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.13887
Recent breakthroughs in Vision-Language (V&L) joint research have achieved remarkable results in various text-driven tasks. High-quality Text-to-video (T2V), a task that has been long considered mission-impossible, was proven feasible with reasonably good results in latest works. However, the resulting videos often have undesired artifacts largely because the system is purely data-driven and agnostic to the physical laws. To tackle this issue and further push T2V towards high-level physical realism, we present an autonomous data generation technique and a dataset, which intend to narrow the gap with a large number of multi-modal, 3D Text-to-Video/Simulation (T2V/S) data. In the dataset, we provide high-resolution 3D physical simulations for both solids and fluids, along with textual descriptions of the physical phenomena. We take advantage of state-of-the-art physical simulation methods (i) Incremental Potential Contact (IPC) and (ii) Material Point Method (MPM) to simulate diverse scenarios, including elastic deformations, material fractures, collisions, turbulence, etc. Additionally, high-quality, multi-view rendering videos are supplied for the benefit of T2V, Neural Radiance Fields (NeRF), and other communities. This work is the first step towards fully automated Text-to-Video/Simulation (T2V/S). Live examples and subsequent work are at https://sites.google.com/view/tpa-net.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2211.13887
- https://arxiv.org/pdf/2211.13887
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4310282974
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4310282974Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2211.13887Digital Object Identifier
- Title
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TPA-Net: Generate A Dataset for Text to Physics-based AnimationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-11-25Full publication date if available
- Authors
-
Yuxing Qiu, Feng Gao, Minchen Li, Govind Thattai, Yin Yang, Chenfanfu JiangList of authors in order
- Landing page
-
https://arxiv.org/abs/2211.13887Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2211.13887Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2211.13887Direct OA link when available
- Concepts
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Computer science, Rendering (computer graphics), Animation, Radiance, Computer graphics (images), Physical law, Artificial intelligence, Optics, Epistemology, Physics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.high-quality, | 149 |
| abstract_inverted_index.Vision-Language | 3 |
| abstract_inverted_index.high-resolution | 100 |
| abstract_inverted_index.state-of-the-art | 121 |
| abstract_inverted_index.mission-impossible, | 25 |
| abstract_inverted_index.Text-to-Video/Simulation | 92, 176 |
| abstract_inverted_index.https://sites.google.com/view/tpa-net. | 185 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |