1 research outputs found
Deferred Neural Rendering: Image Synthesis using Neural Textures
The modern computer graphics pipeline can synthesize images at remarkable
visual quality; however, it requires well-defined, high-quality 3D content as
input. In this work, we explore the use of imperfect 3D content, for instance,
obtained from photo-metric reconstructions with noisy and incomplete surface
geometry, while still aiming to produce photo-realistic (re-)renderings. To
address this challenging problem, we introduce Deferred Neural Rendering, a new
paradigm for image synthesis that combines the traditional graphics pipeline
with learnable components. Specifically, we propose Neural Textures, which are
learned feature maps that are trained as part of the scene capture process.
Similar to traditional textures, neural textures are stored as maps on top of
3D mesh proxies; however, the high-dimensional feature maps contain
significantly more information, which can be interpreted by our new deferred
neural rendering pipeline. Both neural textures and deferred neural renderer
are trained end-to-end, enabling us to synthesize photo-realistic images even
when the original 3D content was imperfect. In contrast to traditional,
black-box 2D generative neural networks, our 3D representation gives us
explicit control over the generated output, and allows for a wide range of
application domains. For instance, we can synthesize temporally-consistent
video re-renderings of recorded 3D scenes as our representation is inherently
embedded in 3D space. This way, neural textures can be utilized to coherently
re-render or manipulate existing video content in both static and dynamic
environments at real-time rates. We show the effectiveness of our approach in
several experiments on novel view synthesis, scene editing, and facial
reenactment, and compare to state-of-the-art approaches that leverage the
standard graphics pipeline as well as conventional generative neural networks.Comment: Video: https://youtu.be/z-pVip6WeyY SIGGRAPH 201