We introduce a novel representation for efficient classical rendering of photorealistic 3D face avatars. Leveraging recent advances in radiance fields anchored to parametric face models, our approach achieves controllable volumetric rendering of complex facial features, including hair, skin, and eyes. At enrollment time, we learn a set of radiance manifolds in 3D space to extract an explicit layered mesh, along with appearance and warp textures. During deployment, this allows us to control and animate the face through simple linear blending and alpha compositing of textures over a static mesh. This explicit representation also enables the generated avatar to be efficiently streamed online and then rendered using classical mesh and shader-based rendering on legacy graphics platforms, eliminating the need for any custom engineering or integration. https://syntec-research.github.io/LegacyAvatars/

We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures. To compress the TSDF, our method relies on a block-based neural network architecture trained end-to-end, achieving state-of-the-art rate-distortion trade-off. To prevent topological errors, we losslessly com- press the signs of the TSDF, which also upper bounds the reconstruction error by the voxel size. To compress the corresponding texture, we designed a fast block-based UV parameterization, generating coherent texture maps that can be effectively compressed using existing video compression algorithms. We demonstrate the performance of our algo- rithms on two 4D performance capture datasets, reducing bitrate by 66% for the same distortion, or alternatively re- ducing the distortion by 50% for the same bitrate, compared to the state-of-the-art.

The increasing demand for 3D content in augmented and virtual reality has motivated the development of volumetric performance capture systemsnsuch as the Light Stage. Recent advances are pushing free viewpoint relightable videos of dynamic human performances closer to photorealistic quality. However, despite significant efforts, these sophisticated systems are limited by reconstruction and rendering algorithms which do not fully model complex 3D structures and higher order light transport effects such as global illumination and sub-surface scattering. In this paper, we propose a system that combines traditional geometric pipelines with a neural rendering scheme to generate photorealistic renderings of dynamic performances under desired viewpoint and lighting. Our system leverages deep neural networks that model the classical rendering process to learn implicit features that represent the view-dependent appearance of the subject independent of the geometry layout, allowing for generalization to unseen subject poses and even novel subject identity. Detailed experiments and comparisons demonstrate the efficacy and versatility of our method to generate high-quality results, significantly outperforming the existing state-of-the-art solutions.

Rotate-Scale-Translate (RST) interactions have become the de facto standard when interacting with two-dimensional (2D) contexts in single-touch and multi-touch environments. Because the use of RST has thus far focused almost entirely on 2D, there are not yet standard techniques for extending these principles into three dimensions. In this paper we describe a screen-space method which fully captures the semantics of the traditional 2D RST multi-touch interaction, but also allows us to extend these same principles into three-dimensional (3D) interaction. Just like RST allows users to directly manipulate 2D contexts with two or more points, our method allows the user to directly manipulate 3D objects with three or more points. We show some novel interactions, which take perspective into account and are thus not available in orthographic environments. Furthermore, we identify key ambiguities and unexpected behaviors that arise when performing direct manipulation in 3D and offer solutions to mitigate the difficulties each presents. Finally, we show how to extend our method to meet application-specific control objectives, as well as show our method working in some example environments.

We demonstrate a pressure-sensitive depth sorting technique that extends standard two-dimensional (2D) manipulation techniques, particularly those used with multi-touch or multi-point controls. We combine this layering operation with a page-folding metaphor for more fluid interaction in applications requiring 2D sorting and layout.

The creation of most models used in computer animation and computer games requires the assignment of texture coordinates, texture painting, and texture editing. We present a novel approach for texture placement and editing based on direct manipulation of textures on the surface. Compared to conventional tools for surface texturing, our system combines UV-coordinate specification and texture editing into one seamless process, reducing the need for careful initial design of parameterization and providing a natural interface for working with textures directly on 3D surfaces.A combination of efficient techniques for interactive constrained parameterization and advanced input devices makes it possible to realize a set of natural interaction paradigms. The texture is regarded as a piece of stretchable material, which the user can position and deform on the surface, selecting arbitrary sets of constraints and mapping texture points to the surface; in addition, the multi-touch input makes it possible to specify natural handles for texture manipulation using point constraints associated with different fingers. Pressure can be used as a direct interface for texture combination operations. The 3D position of the object and its texture can be manipulated simultaneously using two-hand input.