PLA4D: Pixel-Level Alignments for Text-to-4D Gaussian Splatting

• URL: http://arxiv.org/abs/2405.19957v4
• Date: Tue, 19 Nov 2024 02:12:54 GMT
• Title: PLA4D: Pixel-Level Alignments for Text-to-4D Gaussian Splatting
• Authors: Qiaowei Miao, JinSheng Quan, Kehan Li, Yawei Luo,
• Abstract summary: Previous text-to-4D methods have leveraged multiple Score Distillation Sampling (SDS) techniques. We introduce textbfPixel-textbfLevel textbfAlignment for text-driven textbf4D Gaussian splatting (PLA4D) PLA4D provides an anchor reference, i.e., text-generated video, to align the rendering process conditioned by different DMs in pixel space.
• Score: 9.517058280333806
• License:
• Abstract: Previous text-to-4D methods have leveraged multiple Score Distillation Sampling (SDS) techniques, combining motion priors from video-based diffusion models (DMs) with geometric priors from multiview DMs to implicitly guide 4D renderings. However, differences in these priors result in conflicting gradient directions during optimization, causing trade-offs between motion fidelity and geometry accuracy, and requiring substantial optimization time to reconcile the models. In this paper, we introduce \textbf{P}ixel-\textbf{L}evel \textbf{A}lignment for text-driven \textbf{4D} Gaussian splatting (PLA4D) to resolve this motion-geometry conflict. PLA4D provides an anchor reference, i.e., text-generated video, to align the rendering process conditioned by different DMs in pixel space. For static alignment, our approach introduces a focal alignment method and Gaussian-Mesh contrastive learning to iteratively adjust focal lengths and provide explicit geometric priors at each timestep. At the dynamic level, a motion alignment technique and T-MV refinement method are employed to enforce both pose alignment and motion continuity across unknown viewpoints, ensuring intrinsic geometric consistency across views. With such pixel-level multi-DM alignment, our PLA4D framework is able to generate 4D objects with superior geometric, motion, and semantic consistency. Fully implemented with open-source tools, PLA4D offers an efficient and accessible solution for high-quality 4D digital content creation with significantly reduced generation time.

Related papers

• Trans4D: Realistic Geometry-Aware Transition for Compositional Text-to-4D Synthesis [60.853577108780414]
  Existing 4D generation methods can generate high-quality 4D objects or scenes based on user-friendly conditions. We propose Trans4D, a novel text-to-4D synthesis framework that enables realistic complex scene transitions. In experiments, Trans4D consistently outperforms existing state-of-the-art methods in generating 4D scenes with accurate and high-quality transitions.
  arXiv  Detail & Related papers  (2024-10-09T17:56:03Z)
• CT4D: Consistent Text-to-4D Generation with Animatable Meshes [53.897244823604346]
  We present a novel framework, coined CT4D, which directly operates on animatable meshes for generating consistent 4D content from arbitrary user-supplied prompts. Our framework incorporates a unique Generate-Refine-Animate (GRA) algorithm to enhance the creation of text-aligned meshes. Our experimental results, both qualitative and quantitative, demonstrate that our CT4D framework surpasses existing text-to-4D techniques in maintaining interframe consistency and preserving global geometry.
  arXiv  Detail & Related papers  (2024-08-15T14:41:34Z)
• Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models [116.31344506738816]
  We present a novel framework, textbfDiffusion4D, for efficient and scalable 4D content generation. We develop a 4D-aware video diffusion model capable of synthesizing orbital views of dynamic 3D assets. Our method surpasses prior state-of-the-art techniques in terms of generation efficiency and 4D geometry consistency.
  arXiv  Detail & Related papers  (2024-05-26T17:47:34Z)
• SC4D: Sparse-Controlled Video-to-4D Generation and Motion Transfer [57.506654943449796]
  We propose an efficient, sparse-controlled video-to-4D framework named SC4D that decouples motion and appearance. Our method surpasses existing methods in both quality and efficiency. We devise a novel application that seamlessly transfers motion onto a diverse array of 4D entities.
  arXiv  Detail & Related papers  (2024-04-04T18:05:18Z)
• STAG4D: Spatial-Temporal Anchored Generative 4D Gaussians [36.83603109001298]
  STAG4D is a novel framework that combines pre-trained diffusion models with dynamic 3D Gaussian splatting for high-fidelity 4D generation. We show that our method outperforms prior 4D generation works in rendering quality, spatial-temporal consistency, and generation robustness.
  arXiv  Detail & Related papers  (2024-03-22T04:16:33Z)
• 4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency [118.15258850780417]
  This work introduces 4DGen, a novel framework for grounded 4D content creation. We identify static 3D assets and monocular video sequences as key components in constructing the 4D content. Our pipeline facilitates conditional 4D generation, enabling users to specify geometry (3D assets) and motion (monocular videos)
  arXiv  Detail & Related papers  (2023-12-28T18:53:39Z)
• Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models [94.07744207257653]
  We focus on the underexplored text-to-4D setting and synthesize dynamic, animated 3D objects. We combine text-to-image, text-to-video, and 3D-aware multiview diffusion models to provide feedback during 4D object optimization.
  arXiv  Detail & Related papers  (2023-12-21T11:41:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.