Neural Human Deformation Transfer
This addresses the problem of retargeting poses between characters in computer graphics and animation, offering a more flexible approach than traditional methods that require explicit pose definitions, though it is incremental in its neural architecture application.
The paper tackles human deformation transfer by transforming a character's identity without altering its pose, using a neural encoder-decoder architecture that encodes identity features and conditions the decoder on pose. The method outperforms state-of-the-art approaches quantitatively and qualitatively, generalizes better to unseen poses, and includes a fine-tuning step for extreme identities and simple clothing transfer.
We consider the problem of human deformation transfer, where the goal is to retarget poses between different characters. Traditional methods that tackle this problem require a clear definition of the pose, and use this definition to transfer poses between characters. In this work, we take a different approach and transform the identity of a character into a new identity without modifying the character's pose. This offers the advantage of not having to define equivalences between 3D human poses, which is not straightforward as poses tend to change depending on the identity of the character performing them, and as their meaning is highly contextual. To achieve the deformation transfer, we propose a neural encoder-decoder architecture where only identity information is encoded and where the decoder is conditioned on the pose. We use pose independent representations, such as isometry-invariant shape characteristics, to represent identity features. Our model uses these features to supervise the prediction of offsets from the deformed pose to the result of the transfer. We show experimentally that our method outperforms state-of-the-art methods both quantitatively and qualitatively, and generalises better to poses not seen during training. We also introduce a fine-tuning step that allows to obtain competitive results for extreme identities, and allows to transfer simple clothing.