![]() Consequently, we construct the 3D avatar in a canonical ![]() Finally, to train NeRF with the images thatĪre viewpoint-aware yet are not strictly consistent in geometry, our approachĬonsiders per-image geometric variation as a view of deformation from a sharedģD canonical space. Viewpoint-aware images contain identical textures on identical pixel positions Texture problem we observed from our empirical analysis, where the Latent of the diffusion model in order to ameliorate the viewpoint-agnostic ![]() We also conduct low-pass filtering of Gaussian When generating the viewpoint-aware images, we utilize cross-referenceĪttention to inject well-controlled, referential facial expression andĪppearance via cross attention. Optimized with a set of controlled viewpoint-aware images that we generate fromĬontrolNet, whose condition input is the depth map extracted from the input ![]() Strategy is to construct the 3D avatar in Neural Radiance Fields (NeRF) Given a monocular video casually captured with hand-held camera. Text-to-3D avatar generation method whose facial expression is controllable In response, we propose Text2Control3D, a controllable None of them addresses the question of adding such controllability to Geometrically controllable, high-fidelity text-to-image generation. Download a PDF of the paper titled Text2Control3D: Controllable 3D Avatar Generation in Neural Radiance Fields using Geometry-Guided Text-to-Image Diffusion Model, by Sungwon Hwang and 2 other authors Download PDF Abstract: Recent advances in diffusion models such as ControlNet have enabled ![]()
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