Despite their impressive visual fidelity, existing personalized generative models lack interactive control over spatial composition and scale poorly to multiple subjects. To address these limitations, we present LayerComposer, an interactive …
Generating high-fidelity images of humans with fine-grained control over attributes such as hairstyle and clothing remains a core challenge in personalized text-to-image synthesis. While prior methods emphasize identity preservation from a reference …
This paper addresses a challenging question: How can we efficiently create high-quality, wide-scope 3D scenes from a single arbitrary image? Existing methods face several constraints, such as requiring multi-view data, time-consuming per-scene …
We introduce Omni-ID, a novel facial representation designed specifically for generative tasks. Omni-ID encodes holistic information about an individual's appearance across diverse expressions and poses within a fixed-size representation. It …
In this work, we analyze camera motion from a first principles perspective, uncovering insights that enable precise 3D camera manipulation without compromising synthesis quality. First, we determine that motion induced by camera movements in videos …
We introduce Amortized Text-to-Mesh (AToM), a feed-forward text-to-mesh framework optimized across multiple text prompts simultaneously. In contrast to existing text-to-3D methods that often entail time-consuming per-prompt optimization and commonly …
We present “Magic123”, a two-stage coarse-to-fine solution for high-quality, tex-tured 3D meshes generation from a single unposed image in the wild using both 2D and 3D priors. In the first stage, we optimize a coarse neural radiance field and focus …
Upsampling sparse, noisy, and non-uniform point clouds is a challenging task. In this paper, we propose 3 novel point upsampling modules: Multi-branch GCN, Clone GCN, and NodeShuffle. Our modules use Graph Convolutional Networks (GCNs) to better …