CC3D (ICCV 2023) with Jeong Joon Park on Talking Papers Podcast

Join us on this exciting episode of the Talking Papers Podcast as we sit down with the brilliant Jeong Joon Park to explore his groundbreaking paper, "CC3D: Layout-Conditioned Generation of Compositional 3D Scenes," just published at ICCV 2023.

Discover CC3D, a game-changing conditional generative model redefining 3D scene synthesis. Unlike traditional 3D GANs, CC3D boldly crafts complex scenes with multiple objects, guided by 2D semantic layouts. With a novel 3D field representation, CC3D delivers efficiency and superior scene quality. Get ready for a deep dive into the future of 3D scene generation.

My journey with Jeong Joon Park began with his influential SDF paper at CVPR 2019. We met in person at CVPR 2022, thanks to mutual guest Despoina, who was also a guest on our podcast. Now, as Assistant Professor at the University of Michigan CSE, JJ leads research in realistic 3D content generation, offering opportunities for students to contribute to the frontiers of computer vision and AI.

Don't miss this insightful exploration of this ICCV 2023 paper and the future of 3D scene synthesis.


CC3D: Layout-Conditioned Generation of Compositional 3D Scenes

Authors
Sherwin Bahmani, Jeong Joon Park, Despoina Paschalidou, Xingguang Yan, Gordon Wetzstein, Leonidas Guibas, Andrea Tagliasacchi

Abstract
In this work, we introduce CC3D, a conditional generative model that synthesizes complex 3D scenes conditioned on 2D semantic scene layouts, trained using single-view images. Different from most existing 3D GANs that limit their applicability to aligned single objects, we focus on generating complex scenes with multiple objects, by modeling the compositional nature of 3D scenes. By devising a 2D layout-based approach for 3D synthesis and implementing a new 3D field representation with a stronger geometric inductive bias, we have created a 3D GAN that is both efficient and of high quality, while allowing for a more controllable generation process. Our evaluations on synthetic 3D-FRONT and real-world KITTI-360 datasets demonstrate that our model generates scenes of improved visual and geometric quality in comparison to previous works.

TIME STAMPS
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00:00 CC3D
01:07 Authors
02:51Abstract
05:22 Introduction
11:09 Contribution
13:55 Related work
18:02 Approach
38:58 Results
48:52 Conclusions and future work
52:23 What did reviewer 2 say?


CONTACT

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