Track, Inpaint, Resplat: Subject-driven 3D and 4D Generation with
  Progressive Texture Infilling
Current 3D/4D generation methods are usually optimized for photorealism,
efficiency, and aesthetics. However, they often fail to preserve the semantic
identity of the subject across different viewpoints. Adapting generation
methods with one or few images of a specific subject (also known as
Personalization or Subject-driven generation) allows generating visual content
that align with the identity of the subject. However, personalized 3D/4D
generation is still largely underexplored. In this work, we introduce TIRE
(Track, Inpaint, REsplat), a novel method for subject-driven 3D/4D generation.
It takes an initial 3D asset produced by an existing 3D generative model as
input and uses video tracking to identify the regions that need to be modified.
Then, we adopt a subject-driven 2D inpainting model for progressively infilling
the identified regions. Finally, we resplat the modified 2D multi-view
observations back to 3D while still maintaining consistency. Extensive
experiments demonstrate that our approach significantly improves identity
preservation in 3D/4D generation compared to state-of-the-art methods. Our
project website is available at
https://zsh2000.github.io/track-inpaint-resplat.github.io/.
69003a8622d452aac6dd439e
Shuhong Zheng
claimed_verified
2025-10-29T12:50:01.869Z
64361417a4bd75c62cc1e534
/avatars/edf6ac3fb9bd63ceb78f3b95254e6b19.svg
Shuhong Zheng
ShuhongZheng
user
ShuhongZheng
69003a8622d452aac6dd439f
Ashkan Mirzaei
69003a8622d452aac6dd43a0
Igor Gilitschenski
Track, Inpaint, Resplat: Subject-driven 3D and 4D Generation with
  Progressive Texture Infilling
Current 3D/4D generation methods are usually optimized for photorealism,
efficiency, and aesthetics. However, they often fail to preserve the semantic
identity of the subject across different viewpoints. Adapting generation
methods with one or few images of a specific subject (also known as
Personalization or Subject-driven generation) allows generating visual content
that align with the identity of the subject. However, personalized 3D/4D
generation is still largely underexplored. In this work, we introduce TIRE
(Track, Inpaint, REsplat), a novel method for subject-driven 3D/4D generation.
It takes an initial 3D asset produced by an existing 3D generative model as
input and uses video tracking to identify the regions that need to be modified.
Then, we adopt a subject-driven 2D inpainting model for progressively infilling
the identified regions. Finally, we resplat the modified 2D multi-view
observations back to 3D while still maintaining consistency. Extensive
experiments demonstrate that our approach significantly improves identity
preservation in 3D/4D generation compared to state-of-the-art methods. Our
project website is available at
https://zsh2000.github.io/track-inpaint-resplat.github.io/.
TIRE, a novel method for subject-driven 3D/4D generation, enhances identity preservation by tracking, inpainting, and resplatting regions of a 3D asset.
3D generative model video tracking subject-driven 2D inpainting resplatting 3D/4D generation identity preservation