Zobrazeno 1 - 10
of 212
pro vyhledávání: '"Debevec, Paul"'
Autor:
He, Mingming, Clausen, Pascal, Taşel, Ahmet Levent, Ma, Li, Pilarski, Oliver, Xian, Wenqi, Rikker, Laszlo, Yu, Xueming, Burgert, Ryan, Yu, Ning, Debevec, Paul
We present a novel framework for free-viewpoint facial performance relighting using diffusion-based image-to-image translation. Leveraging a subject-specific dataset containing diverse facial expressions captured under various lighting conditions, in
Externí odkaz:
http://arxiv.org/abs/2410.08188
We introduce Magenta Green Screen, a novel machine learning--enabled matting technique for recording the color image of a foreground actor and a simultaneous high-quality alpha channel without requiring a special camera or manual keying techniques. W
Externí odkaz:
http://arxiv.org/abs/2306.13702
While the LED panels used in virtual production systems can display vibrant imagery with a wide color gamut, they produce problematic color shifts when used as lighting due to their peaky spectral output from narrow-band red, green, and blue LEDs. In
Externí odkaz:
http://arxiv.org/abs/2205.12403
Autor:
Debevec, Paul, LeGendre, Chloe
We present a technique to reduce the dynamic range of an HDRI lighting environment map in an efficient, energy-preserving manner by spreading out the light of concentrated light sources. This allows us to display a reasonable approximation of the ill
Externí odkaz:
http://arxiv.org/abs/2205.07873
Autor:
Zhang, Xiuming, Srinivasan, Pratul P., Deng, Boyang, Debevec, Paul, Freeman, William T., Barron, Jonathan T.
We address the problem of recovering the shape and spatially-varying reflectance of an object from multi-view images (and their camera poses) of an object illuminated by one unknown lighting condition. This enables the rendering of novel views of the
Externí odkaz:
http://arxiv.org/abs/2106.01970
We present a learning-based method for estimating 4D reflectance field of a person given video footage illuminated under a flat-lit environment of the same subject. For training data, we use one light at a time to illuminate the subject and capture t
Externí odkaz:
http://arxiv.org/abs/2104.02773
Neural volumetric representations such as Neural Radiance Fields (NeRF) have emerged as a compelling technique for learning to represent 3D scenes from images with the goal of rendering photorealistic images of the scene from unobserved viewpoints. H
Externí odkaz:
http://arxiv.org/abs/2103.14645
Autor:
Sun, Tiancheng, Xu, Zexiang, Zhang, Xiuming, Fanello, Sean, Rhemann, Christoph, Debevec, Paul, Tsai, Yun-Ta, Barron, Jonathan T., Ramamoorthi, Ravi
The light stage has been widely used in computer graphics for the past two decades, primarily to enable the relighting of human faces. By capturing the appearance of the human subject under different light sources, one obtains the light transport mat
Externí odkaz:
http://arxiv.org/abs/2010.08888
Autor:
Zhang, Xiuming, Fanello, Sean, Tsai, Yun-Ta, Sun, Tiancheng, Xue, Tianfan, Pandey, Rohit, Orts-Escolano, Sergio, Davidson, Philip, Rhemann, Christoph, Debevec, Paul, Barron, Jonathan T., Ramamoorthi, Ravi, Freeman, William T.
The light transport (LT) of a scene describes how it appears under different lighting and viewing directions, and complete knowledge of a scene's LT enables the synthesis of novel views under arbitrary lighting. In this paper, we focus on image-based
Externí odkaz:
http://arxiv.org/abs/2008.03806
Autor:
LeGendre, Chloe, Ma, Wan-Chun, Pandey, Rohit, Fanello, Sean, Rhemann, Christoph, Dourgarian, Jason, Busch, Jay, Debevec, Paul
We present a learning-based technique for estimating high dynamic range (HDR), omnidirectional illumination from a single low dynamic range (LDR) portrait image captured under arbitrary indoor or outdoor lighting conditions. We train our model using
Externí odkaz:
http://arxiv.org/abs/2008.02396