Search Results for author:
Found 10 papers, 2 papers with code
Neural Relighting with Subsurface Scattering by Learning the Radiance Transfer Gradient
Reconstructing and relighting objects and scenes under varying lighting conditions is challenging: existing neural rendering methods often cannot handle the complex interactions between materials and light.
Voxel-informed Language Grounding
Natural language applied to natural 2D images describes a fundamentally 3D world.
Differentiable Gradient Sampling for Learning Implicit 3D Scene Reconstructions from a Single Image
Existing approaches for single object reconstruction impose supervision signals based on the loss of the signed distance value from all locations in a scene, posing difficulties when extending to real-world scenarios.
Minimax Active Learning
Active learning aims to develop label-efficient algorithms by querying the most representative samples to be labeled by a human annotator.
Be Your Own Prada: Fashion Synthesis with Structural Coherence
In the second stage, a generative model with a newly proposed compositional mapping layer is used to render the final image with precise regions and textures conditioned on this map.
Deep Cascaded Bi-Network for Face Hallucination
We present a novel framework for hallucinating faces of unconstrained poses and with very low resolution (face size as small as 5pxIOD).
Ranked #5 on
Image Super-Resolution
on VggFace2 - 8x upscaling
Unconstrained Face Alignment via Cascaded Compositional Learning
We present a practical approach to address the problem of unconstrained face alignment for a single image.
Ranked #19 on
Face Alignment
on AFLW-19
Towards Arbitrary-View Face Alignment by Recommendation Trees
The unified framework seamlessly handles different viewpoints and landmark protocols, and it is trained by optimising directly on landmark locations, thus yielding superior results on arbitrary-view face alignment.
Face alignment by coarse-to-fine shape searching
We present a novel face alignment framework based on coarse-to-fine shape searching.
Ranked #20 on
Face Alignment
on AFLW-19
Transferring Landmark Annotations for Cross-Dataset Face Alignment
We show extensive results on combining various popular databases (LFW, AFLW, LFPW, HELEN) for improved cross-dataset and unseen data alignment.
