We lifted 15 functions out of this paper's own repositories and ran 11 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 |
|---|---|---|
| ankanbhunia/PIDM | canonical | 11 of 15 |
| Function | Status | Where it lives |
|---|---|---|
| apply_conditions | Ran | ankanbhunia/PIDM/models/blocks.py code served (permissive licence) · get_code("f665a07118db6408") |
| approx_standard_normal_cdf | Ran | ankanbhunia/PIDM/models/losses.py code served (permissive licence) · get_code("cfd76fd0d89574a4") |
| compute_alpha | Ran | ankanbhunia/PIDM/diffusion.py code served (permissive licence) · get_code("9215fb189fbfb9f4") |
| conv2d | Ran | ankanbhunia/PIDM/model.py code served (permissive licence) · get_code("73c8d2526f595b71") |
| discretized_gaussian_log_likelihood | Ran | ankanbhunia/PIDM/models/losses.py code served (permissive licence) · get_code("f75421bbdc7dba43") |
| linear | Ran | ankanbhunia/PIDM/model.py code served (permissive licence) · get_code("b9aebc971fbddb2e") |
| make_beta_schedule | Ran | ankanbhunia/PIDM/diffusion.py code served (permissive licence) · get_code("9334cc544f75fcef") |
| mean_flat | Ran | ankanbhunia/PIDM/models/nn.py code served (permissive licence) · get_code("f6d7c009a8efb8b7") |
| normal_kl | Ran | ankanbhunia/PIDM/models/losses.py code served (permissive licence) · get_code("8afbfc42c6ea0448") |
| prob_mask_like | Ran | ankanbhunia/PIDM/models/unet_autoenc.py code served (permissive licence) · get_code("3c6433dd421724e0") |
| variance_scaling_init_ | Ran | ankanbhunia/PIDM/model.py code served (permissive licence) · get_code("264892ef469646de") |
| avg_pool_nd | Not yet run | ankanbhunia/PIDM/models/nn.py code served (permissive licence) · get_code("ecd0fc28815b65ae") |
| conv_nd | Not yet run | ankanbhunia/PIDM/models/nn.py code served (permissive licence) · get_code("fe4eb545bbb728e0") |
| ddim_steps | Not yet run | ankanbhunia/PIDM/diffusion.py code served (permissive licence) · get_code("74c97198d468e97e") |
| jsonable | Not yet run | ankanbhunia/PIDM/models/config_base.py code served (permissive licence) · get_code("3436fefeb3a4b49a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The pose-guided person image generation task requires synthesizing photorealistic images of humans in arbitrary poses. The existing approaches use generative adversarial networks that do not necessarily maintain realistic textures or need dense correspondences that struggle to handle complex deformations and severe occlusions. In this work, we show how denoising diffusion models can be applied for high-fidelity person image synthesis with strong sample diversity and enhanced mode coverage of the learnt data distribution. Our proposed Person Image Diffusion Model (PIDM) disintegrates the complex transfer problem into a series of simpler forward-backward denoising steps. This helps in learning plausible source-to-target transformation trajectories that result in faithful textures and undistorted appearance details. We introduce a 'texture diffusion module' based on cross-attention to accurately model the correspondences between appearance and pose information available in source and target images. Further, we propose 'disentangled classifier-free guidance' to ensure close resemblance between the conditional inputs and the synthesized output in terms of both pose and appearance information. Our extensive results on two large-scale benchmarks and a user study demonstrate the photorealism of our proposed approach under challenging scenarios. We also show how our generated images can help in downstream tasks. Our code and models will be publicly released.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2211.12500")
get_code_for_paper("2211.12500")
have("2211.12500")
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