Abstract
Photorealistic animation is a desirable technique for computer games and movie production. We propose a new method to synthesize plausible videos of human actors with new motions using a single cheap RGB-D camera. A small database is captured in a usual office environment, which happens only once for synthesizing different motions. We propose a marker-less performance capture method using sparse deformation to obtain the geometry and pose of the actor for each time instance in the database. Then, we synthesize an animation video of the actor performing the new motion that is defined by the user. An adaptive model-guided texture synthesis method based on weighted low-rank matrix completion is proposed to be less sensitive to noise and outliers, which enables us to easily create photorealistic animation videos with new motions that are different from the motions in the database. Experimental results on the public data set and our captured data set have verified the effectiveness of the proposed method.
Highlights
P HOTOREALISTIC animation aims to create a plausible photorealistic video of an actor performing a new motion based on a database, which is highly desirable for both computer games and movie production [1]ā[3]
We present a sparse deformation optimization method to make the marker-less performance capture less affected by noise and outliers, which gives a pivotal constraint for video synthesis
3) Adaptive Sparse Texture Synthesis Method: To recover the textures from limited data set with different motions, we propose an adaptive weighted low-rank matrix completion method
Summary
P HOTOREALISTIC animation aims to create a plausible photorealistic video of an actor performing a new motion based on a database, which is highly desirable for both computer games and movie production [1]ā[3]. We propose a new method to synthesize photorealistic videos of human actors with user-defined motions based on a small database captured by a single RGB-D camera in a usual office environment, allowing a small change of viewpoint. 3) Adaptive Sparse Texture Synthesis Method: To recover the textures from limited data set with different motions, we propose an adaptive weighted low-rank matrix completion method. Through this method, is the frame that has high percentages of missing data recovered, and the noise and outliers in the initially estimated image are reduced.
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