title: PLA4D: Pixel-Level Alignments for Text-to-4D Gaussian Splatting
summary: As text-conditioned diffusion models (DMs) achieve breakthroughs in image,
  video, and 3D generation, the research community's focus has shifted to the
  more challenging task of text-to-4D synthesis, which introduces a temporal
  dimension to generate dynamic 3D objects. In this context, we identify Score
  Distillation Sampling (SDS), a widely used technique for text-to-3D synthesis,
  as a significant hindrance to text-to-4D performance due to its Janus-faced and
  texture-unrealistic problems coupled with high computational costs. In this
  paper, we propose Pixel-Level Alignments for
  Text-to-4D Gaussian Splatting (PLA4D), a novel method that
  utilizes text-to-video frames as explicit pixel alignment targets to generate
  static 3D objects and inject motion into them. Specifically, we introduce Focal
  Alignment to calibrate camera poses for rendering and GS-Mesh Contrastive
  Learning to distill geometry priors from rendered image contrasts at the pixel
  level. Additionally, we develop Motion Alignment using a deformation network to
  drive changes in Gaussians and implement Reference Refinement for smooth 4D
  object surfaces. These techniques enable 4D Gaussian Splatting to align
  geometry, texture, and motion with generated videos at the pixel level.
  Compared to previous methods, PLA4D produces synthesized outputs with better
  texture details in less time and effectively mitigates the Janus-faced problem.
  PLA4D is fully implemented using open-source models, offering an accessible,
  user-friendly, and promising direction for 4D digital content creation. Our
  project page:
  https://github.com/MiaoQiaowei/PLA4D.github.io{https://github.com/MiaoQiaowei/PLA4D.github.io}.
paper_authors._id: 66594d28b8c81d7357289946
paper_authors.name: Qiaowei Miao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-31T10:06:27.770Z
paper_authors._id: 63dcb90622cc06e76a8341eb
paper_authors.avatarUrl: /avatars/59a82bae9792df53af098972285ec19a.svg
paper_authors.fullname: Qiaowei Miao
paper_authors.type: user
paper_authors.user: wdmmqw
paper_authors._id: 66594d28b8c81d7357289947
paper_authors.name: Yawei Luo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-02T18:47:25.711Z
paper_authors._id: 665bfc4c88912c5ab646c3c9
paper_authors.avatarUrl: /avatars/db284af3be943b5786cf0026279ef3b9.svg
paper_authors.fullname: Yawei Luo
paper_authors.type: user
paper_authors.user: RoyalVane
paper_authors._id: 66594d28b8c81d7357289948
paper_authors.name: Yi Yang
paper_title: PLA4D: Pixel-Level Alignments for Text-to-4D Gaussian Splatting
paper_summary: As text-conditioned diffusion models (DMs) achieve breakthroughs in image,
  video, and 3D generation, the research community's focus has shifted to the
  more challenging task of text-to-4D synthesis, which introduces a temporal
  dimension to generate dynamic 3D objects. In this context, we identify Score
  Distillation Sampling (SDS), a widely used technique for text-to-3D synthesis,
  as a significant hindrance to text-to-4D performance due to its Janus-faced and
  texture-unrealistic problems coupled with high computational costs. In this
  paper, we propose Pixel-Level Alignments for
  Text-to-4D Gaussian Splatting (PLA4D), a novel method that
  utilizes text-to-video frames as explicit pixel alignment targets to generate
  static 3D objects and inject motion into them. Specifically, we introduce Focal
  Alignment to calibrate camera poses for rendering and GS-Mesh Contrastive
  Learning to distill geometry priors from rendered image contrasts at the pixel
  level. Additionally, we develop Motion Alignment using a deformation network to
  drive changes in Gaussians and implement Reference Refinement for smooth 4D
  object surfaces. These techniques enable 4D Gaussian Splatting to align
  geometry, texture, and motion with generated videos at the pixel level.
  Compared to previous methods, PLA4D produces synthesized outputs with better
  texture details in less time and effectively mitigates the Janus-faced problem.
  PLA4D is fully implemented using open-source models, offering an accessible,
  user-friendly, and promising direction for 4D digital content creation. Our
  project page:
  https://github.com/MiaoQiaowei/PLA4D.github.io{https://github.com/MiaoQiaowei/PLA4D.github.io}.
paper_ai_summary: PLA4D advances text-to-4D synthesis by improving texture, geometry, and motion alignment through novel pixel-level techniques.
paper_ai_keywords: diffusion models text-to-4D synthesis Score Distillation Sampling Pixel-Level Alignments for Text-to-4D Gaussian Splatting Focal Alignment GS-Mesh Contrastive Learning Motion Alignment deformation network Reference Refinement 4D Gaussian Splatting text-to-video frames