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Recent breakthroughs in Vision-Language (V&L) joint research have achievedremarkable results in various text-driven tasks. High-quality Text-to-video(T2V), a task that has been long considered mission-impossible, was provenfeasible with reasonably good results in latest works. However, the resultingvideos often have undesired artifacts largely because the system is purelydata-driven and agnostic to the physical laws. To tackle this issue and furtherpush T2V towards high-level physical realism, we present an autonomous datageneration technique and a dataset, which intend to narrow the gap with a largenumber of multi-modal, 3D Text-to-Video/Simulation (T2V/S) data. In thedataset, we provide high-resolution 3D physical simulations for both solids andfluids, along with textual descriptions of the physical phenomena. We takeadvantage of state-of-the-art physical simulation methods (i) IncrementalPotential Contact (IPC) and (ii) Material Point Method (MPM) to simulatediverse scenarios, including elastic deformations, material fractures,collisions, turbulence, etc. Additionally, high-quality, multi-view renderingvideos are supplied for the benefit of T2V, Neural Radiance Fields (NeRF), andother communities. This work is the first step towards fully automatedText-to-Video/Simulation (T2V/S). Live examples and subsequent work are athttps://sites.google.com/view/tpa-net.

TPA-Net: Generate A Dataset for Text to Physics-based Animation