3D-GOI: 3D GAN Omni-Inversion for Multifaceted and Multi-object Editing
Haoran Li, Long Ma, Haolin Shi, Yanbin Hao, Yong Liao, Lechao Cheng,, Pengyuan Zhou
TL;DR
3D-GOI introduces a novel 3D GAN inversion framework that enables multifaceted editing of multiple objects and background in complex scenes, addressing limitations of existing methods.
Contribution
It is the first framework to allow comprehensive, multi-object, and multi-attribute editing by accurately inverting attribute codes in 3D GANs.
Findings
Enables editing of multiple objects and background in 3D scenes.
Achieves accurate inversion of shape, appearance, and pose attributes.
Demonstrates significant improvements in editing flexibility and quality.
Abstract
The current GAN inversion methods typically can only edit the appearance and shape of a single object and background while overlooking spatial information. In this work, we propose a 3D editing framework, 3D-GOI, to enable multifaceted editing of affine information (scale, translation, and rotation) on multiple objects. 3D-GOI realizes the complex editing function by inverting the abundance of attribute codes (object shape/appearance/scale/rotation/translation, background shape/appearance, and camera pose) controlled by GIRAFFE, a renowned 3D GAN. Accurately inverting all the codes is challenging, 3D-GOI solves this challenge following three main steps. First, we segment the objects and the background in a multi-object image. Second, we use a custom Neural Inversion Encoder to obtain coarse codes of each object. Finally, we use a round-robin optimization algorithm to get precise codes…
Peer Reviews
Decision·Submitted to ICLR 2024
This paper focuses the task of GAN inversion problem of different latent codes from multiple objects in a single image. To address the challenges lying in the multiple latent code estimation and optimization, there are two technical strengths: 1. The round-robin Optimization strategy to optimize all codes simultaneously. 2. the Neural Inversion Encoder to encode each code for initial estimation. 3. Extensive experiments that demonstrate their effectiveness
The weaknesses in this paper are also obvious. 1. This paper still follows the framework of GIRAFFE by using its scene decomposition manner and training strategy, even though the authors claim that they have more object properties to encode. 2. Second paragraph of Intro: there are some other methods based on VAEs (Sync2Gen, ICCV'21) and transformers (NeurIPS'21) 3. Sec 3.2, and Intro, there is no need to have so many texts to elaborate on why you use the segmentation method. It is pretty int
1. The paper's emphasis on multi-object and multifaceted editing not only differentiates it but also highlights the immense potential such editing holds for future technologies. 2. The ablation study breaks down different components of the proposed method, such as the Neural Inversion Encoder and the Round-robin Optimization algorithm, shedding light on their individual contributions.
The primary concern with the paper lies in its dependency on multiple stages and components for accuracy, which hints at potential inefficiencies and complexities in the method. Specifically, the Neural Inversion Encoder's inability to precisely predict codes independently underscores a fundamental limitation. This necessitates the round-robin optimization approach, adding another layer of complexity. Furthermore, the model's significant deviations in predicting background codes for multi-object
1. 3D GAN inversion is at its preliminary start and this method is the first paper that studies multi-object 3D GAN inversion, which is an important while under-explored topic. 2. The writing is good and experiments is sound.
1. The overall method still lies in the hybrid optimization method, which requires code tuning after the pre-trained encoder gives a coarse estimation. 2. Lacks qualitative comparison with existing encoder-based method such as E3DGE, only Tab. 1 shows the quantitative comparisions. 3. What are the limitation of this method, and how many objects can this method handle within an image? 4. Also, any editing result based on manipulating the latent space, such as using InterfaceGAN?
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Advanced Vision and Imaging · Robotics and Sensor-Based Localization