Summary of Controlrm: Fast and Controllable 3d Generation Via Large Reconstruction Model, by Hongbin Xu et al.
ControLRM: Fast and Controllable 3D Generation via Large Reconstruction Model
by Hongbin Xu, Weitao Chen, Zhipeng Zhou, Feng Xiao, Baigui Sun, Mike Zheng Shou, Wenxiong Kang
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the long-standing issue of controllability in 3D generation methods, particularly when utilizing score-distillation sampling. Current approaches are hindered by laborious procedures that consume a significant amount of time. To tackle this challenge, the authors propose ControLRM, an end-to-end feed-forward model designed for rapid and controllable 3D generation using a large reconstruction model (LRM). The proposed approach consists of a 2D condition generator, a condition encoding transformer, and a triplane decoder transformer. Instead of training from scratch, the authors advocate for a joint training framework that leverages pre-trained LRM and deep encoding layers. The experiment results demonstrate the strong generalization capacity of the proposed approach on quantitative and qualitative comparisons of 3D controllability and generation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to control how 3D objects are generated, so they can be made quickly and accurately. Right now, this process takes a long time because it involves several steps that need to be done in a specific order. The researchers propose a new approach called ControLRM that combines two different types of information: 2D pictures and 3D models. They also use a special kind of training framework that helps the model learn faster. To make sure their results are fair, they tested the model on three different datasets and compared its performance to other methods. |
Keywords
» Artificial intelligence » Decoder » Distillation » Generalization » Transformer
