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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
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portfolio
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publications
Where is My Spot? Few-shot Image Generation via Latent Subspace Optimization
Published in CVPR, 2023
Image generation relies on massive training data that can hardly produce diverse images of an unseen category according to a few examples. In this paper, we address this dilemma by projecting sparse few-shot samples into a continuous latent space that can potentially generate infinite unseen samples. The rationale behind is that we aim to locate a centroid latent position in a conditional StyleGAN, where the corresponding output image on that centroid can maximize the similarity with the given samples. Although the given samples are unseen for the conditional StyleGAN, we assume the neighboring latent subspace around the centroid belongs to the novel category, and therefore introduce two latent subspace optimization objectives. In the first one we use few-shot samples as positive anchors of the novel class, and adjust the StyleGAN to produce the corresponding results with the new class label condition. The second objective is to govern the generation process from the other way around, by altering the centroid and its surrounding latent subspace for a more precise generation of the novel class. These reciprocal optimization objectives inject a novel class into the StyleGAN latent subspace, and therefore new unseen samples can be easily produced by sampling images from it. Extensive experiments demonstrate superior few-shot generation performances compared with state-of-the-art methods, especially in terms of diversity and generation quality. Code is available at https://github.com/chansey0529/LSO.
Recommended citation: Zheng C, Liu B, Zhang H, et al. Where is my spot? few-shot image generation via latent subspace optimization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023: 3272-3281.
Learning an Interpretable Stylized Subspace for 3D-aware Animatable Artforms
Published in IEEE TVCG, 2024
Throughout history, static paintings have captivated viewers within display frames, yet the possibility of making these masterpieces vividly interactive remains intriguing. This research paper introduces 3DArtmator, a novel approach that aims to represent artforms in a highly interpretable stylized space, enabling 3D-aware animatable reconstruction and editing. Our rationale is to transfer the interpretability and 3D controllability of the latent space in a 3D-aware GAN to a stylized sub-space of a customized GAN, revitalizing the original artforms. To this end, the proposed two-stage optimization framework of 3DArtmator begins with discovering an anchor in the original latent space that accurately mimics the pose and content of a given art painting. This anchor serves as a reliable indicator of the original latent space local structure, therefore sharing the same editable predefined expression vectors. In the second stage, we train a customized 3D-aware GAN specific to the input artform, while enforcing the preservation of the original latent local structure through a meticulous style-directional difference loss. This approach ensures the creation of a stylized sub-space that remains interpretable and retains 3D control. The effectiveness and versatility of 3DArtmator are validated through extensive experiments across a diverse range of art styles. With the ability to generate 3D reconstruction and editing for artforms while maintaining interpretability, 3DArtmator opens up new possibilities for artistic exploration and engagement.
Recommended citation: Zheng C, Liu B, Xu X, et al. Learning an Interpretable Stylized Subspace for 3D-aware Animatable Artforms[J]. IEEE Transactions on Visualization and Computer Graphics, 2024.
Learning with Unreliability: Fast Few-shot Voxel Radiance Fields with Relative Geometric Consistency
Published in CVPR, 2024
We propose a voxel-based optimization framework, ReVoRF, for few-shot radiance fields that strategically address the unreliability in pseudo novel view synthesis. Our method pivots on the insight that relative depth relationships within neighboring regions are more reliable than the absolute color values in disoccluded areas. Consequently, we devise a bilateral geometric consistency loss that carefully navigates the trade-off between color fidelity and geometric accuracy in the context of depth consistency for uncertain regions. Moreover, we present a reliability-guided learning strategy to discern and utilize the variable quality across synthesized views, complemented by a reliability-aware voxel smoothing algorithm that smoothens the transition between reliable and unreliable data patches. Our approach allows for a more nuanced use of all available data, promoting enhanced learning from regions previously considered unsuitable for high-quality reconstruction. Extensive experiments across diverse datasets reveal that our approach attains significant gains in efficiency and accuracy, delivering rendering speeds of 3 FPS, 7 mins to train a 360∘ scene, and a 5\% improvement in PSNR over existing few-shot methods. Code is available at https://github.com/HKCLynn/ReVoRF.
Recommended citation: Xu Y, Liu B, Tang H, et al. Learning with unreliability: fast few-shot voxel radiance fields with relative geometric consistency[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.
Beyond Textual Constraints: Learning Novel Diffusion Conditions with Fewer Examples
Published in CVPR, 2024
In this paper, we delve into a novel aspect of learning novel diffusion conditions with datasets an order of magnitude smaller. The rationale behind our approach is the elimination of textual constraints during the few-shot learning process. To that end, we implement two optimization strategies. The first, prompt-free conditional learning, utilizes a prompt-free encoder derived from a pre-trained Stable Diffusion model. This strategy is designed to adapt new conditions to the diffusion process by minimizing the textual-visual correlation, thereby ensuring a more precise alignment between the generated content and the specified conditions. The second strategy entails condition-specific negative rectification, which addresses the inconsistencies typically brought about by Classifier-free guidance in few-shot training contexts. Our extensive experiments across a variety of condition modalities demonstrate the effectiveness and efficiency of our framework, yielding results comparable to those obtained with datasets a thousand times larger. Our codes are available at https://github.com/Yuyan9Yu/BeyondTextConstraint.
Recommended citation: Yu Y, Liu B, Zheng C, et al. Beyond textual constraints: learning novel diffusion conditions with fewer examples[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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