We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| shrubb/latent-pose-reenactment | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| string_to_valid_filename | Ran | shrubb/latent-pose-reenactment/drive.py code served (permissive licence) · get_code("6845a5c0b495a953") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We propose a neural head reenactment system, which is driven by a latent pose representation and is capable of predicting the foreground segmentation alongside the RGB image. The latent pose representation is learned as a part of the entire reenactment system, and the learning process is based solely on image reconstruction losses. We show that despite its simplicity, with a large and diverse enough training dataset, such learning successfully decomposes pose from identity. The resulting system can then reproduce mimics of the driving person and, furthermore, can perform cross-person reenactment. Additionally, we show that the learned descriptors are useful for other pose-related tasks, such as keypoint prediction and pose-based retrieval.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2004.12000")
get_code_for_paper("2004.12000")
have("2004.12000")
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