22 citations · 41 across the 10 of their papers we have counts for
8 papers · 1 filter
Chimera: Compositional Image Generation using Part-based Concepting
Shivam Singh, Yiming Chen, Agneet Chatterjee +4
Personalized image generative models are highly proficient at synthesizing images from text or a single image, yet they lack explicit control for composing objects from specific pa…
Diffusion Sampling with Momentum for Mitigating Divergence Artifacts
Suttisak Wizadwongsa, Worameth Chinchuthakun, Pramook Khungurn +2
Despite the remarkable success of diffusion models in image generation, slow sampling remains a persistent issue. To accelerate the sampling process, prior studies have reformulate…
ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image Collections
Chun-Han Yao, Amit Raj, Wei-Chih Hung +4
Estimating 3D articulated shapes like animal bodies from monocular images is inherently challenging due to the ambiguities of camera viewpoint, pose, texture, lighting, etc. We pro…
LANe: Lighting-Aware Neural Fields for Compositional Scene Synthesis
Akshay Krishnan, Amit Raj, Xianling Zhang +5
Neural fields have recently enjoyed great success in representing and rendering 3D scenes. However, most state-of-the-art implicit representations model static or dynamic scenes as…
DreamBooth3D: Subject-Driven Text-to-3D Generation
Amit Raj, Srinivas Kaza, Ben Poole +9
We present DreamBooth3D, an approach to personalize text-to-3D generative models from as few as 3-6 casually captured images of a subject. Our approach combines recent advances in…
DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars
Amit Raj, Umar Iqbal, Koki Nagano +4
Acquisition and creation of digital human avatars is an important problem with applications to virtual telepresence, gaming, and human modeling. Most contemporary approaches for av…