39 research outputs found
FDLS: A Deep Learning Approach to Production Quality, Controllable, and Retargetable Facial Performances
Visual effects commonly requires both the creation of realistic synthetic
humans as well as retargeting actors' performances to humanoid characters such
as aliens and monsters. Achieving the expressive performances demanded in
entertainment requires manipulating complex models with hundreds of parameters.
Full creative control requires the freedom to make edits at any stage of the
production, which prohibits the use of a fully automatic ``black box'' solution
with uninterpretable parameters. On the other hand, producing realistic
animation with these sophisticated models is difficult and laborious. This
paper describes FDLS (Facial Deep Learning Solver), which is Weta Digital's
solution to these challenges. FDLS adopts a coarse-to-fine and
human-in-the-loop strategy, allowing a solved performance to be verified and
edited at several stages in the solving process. To train FDLS, we first
transform the raw motion-captured data into robust graph features. Secondly,
based on the observation that the artists typically finalize the jaw pass
animation before proceeding to finer detail, we solve for the jaw motion first
and predict fine expressions with region-based networks conditioned on the jaw
position. Finally, artists can optionally invoke a non-linear finetuning
process on top of the FDLS solution to follow the motion-captured virtual
markers as closely as possible. FDLS supports editing if needed to improve the
results of the deep learning solution and it can handle small daily changes in
the actor's face shape. FDLS permits reliable and production-quality
performance solving with minimal training and little or no manual effort in
many cases, while also allowing the solve to be guided and edited in unusual
and difficult cases. The system has been under development for several years
and has been used in major movies.Comment: DigiPro '22: The Digital Production Symposiu
An intuitive control space for material appearance
Many different techniques for measuring material appearance have been
proposed in the last few years. These have produced large public datasets,
which have been used for accurate, data-driven appearance modeling. However,
although these datasets have allowed us to reach an unprecedented level of
realism in visual appearance, editing the captured data remains a challenge. In
this paper, we present an intuitive control space for predictable editing of
captured BRDF data, which allows for artistic creation of plausible novel
material appearances, bypassing the difficulty of acquiring novel samples. We
first synthesize novel materials, extending the existing MERL dataset up to 400
mathematically valid BRDFs. We then design a large-scale experiment, gathering
56,000 subjective ratings on the high-level perceptual attributes that best
describe our extended dataset of materials. Using these ratings, we build and
train networks of radial basis functions to act as functionals mapping the
perceptual attributes to an underlying PCA-based representation of BRDFs. We
show that our functionals are excellent predictors of the perceived attributes
of appearance. Our control space enables many applications, including intuitive
material editing of a wide range of visual properties, guidance for gamut
mapping, analysis of the correlation between perceptual attributes, or novel
appearance similarity metrics. Moreover, our methodology can be used to derive
functionals applicable to classic analytic BRDF representations. We release our
code and dataset publicly, in order to support and encourage further research
in this direction
ACM Transactions on Graphics
We present FlexMolds, a novel computational approach to automatically design flexible, reusable molds that, once 3D printed, allow us to physically fabricate, by means of liquid casting, multiple copies of complex shapes with rich surface details and complex topology. The approach to design such flexible molds is based on a greedy bottom-up search of possible cuts over an object, evaluating for each possible cut the feasibility of the resulting mold. We use a dynamic simulation approach to evaluate candidate molds, providing a heuristic to generate forces that are able to open, detach, and remove a complex mold from the object it surrounds. We have tested the approach with a number of objects with nontrivial shapes and topologies
Hardware Accelerators for Animated Ray Tracing
Future graphics processors are likely to incorporate hardware accelerators for real-time ray tracing, in order to render increasingly complex lighting effects in interactive applications. However, ray tracing poses difficulties when drawing scenes with dynamic content, such as animated characters and objects. In dynamic scenes, the spatial datastructures used to accelerate ray tracing are invalidated on each animation frame, and need to be rapidly updated. Tree update is a complex subtask in its own right, and becomes highly expensive in complex scenes. Both ray tracing and tree update are highly memory-intensive tasks, and rendering systems are increasingly bandwidth-limited, so research on accelerator hardware has focused on architectural techniques to optimize away off-chip memory traffic. Dynamic scene support is further complicated by the recent introduction of compressed trees, which use low-precision numbers for storage and computation. Such compression reduces both the arithmetic and memory bandwidth cost of ray tracing, but adds to the complexity of tree update.This thesis proposes methods to cope with dynamic scenes in hardware-accelerated ray tracing, with focus on reducing traffic to external memory. Firstly, a hardware architecture is designed for linear bounding volume hierarchy construction, an algorithm which is a basic building block in most state-of-the-art software tree builders. The algorithm is rearranged into a streaming form which reduces traffic to one-third of software implementations of the same algorithm. Secondly, an algorithm is proposed for compressing bounding volume hierarchies in a streaming manner as they are output from a hardware builder, instead of performing compression as a postprocessing pass. As a result, with the proposed method, compression reduces the overall cost of tree update rather than increasing it. The last main contribution of this thesis is an evaluation of shallow bounding volume hierarchies, common in software ray tracing, for use in hardware pipelines. These are found to be more energy-efficient than binary hierarchies. The results in this thesis both confirm that dynamic scene support may become a bottleneck in real time ray tracing, and add to the state of the art on tree update in terms of energy-efficiency, as well as the complexity of scenes that can be handled in real time on resource-constrained platforms