Open gitouni opened 3 months ago
Thanks for sharing the code. I have a question about the implementation of Sampson-guided sampling. As illustrated in Eq (8) in your paper, the probability of p(I|x_t) is a scaled gradient imposed on the noise predicted by the model, which is consistent with this paper. However, for corresponding implementation I found here: https://github.com/facebookresearch/PoseDiffusion/blob/36eeb1654ad8e67844672d7a40e7c179e9c58104/pose_diffusion/util/geometry_guided_sampling.py#L67-L126
https://github.com/facebookresearch/PoseDiffusion/blob/36eeb1654ad8e67844672d7a40e7c179e9c58104/pose_diffusion/util/geometry_guided_sampling.py#L129-L172
It seems that the model_mean is directly optimized by sampson-error rather than placed in the loop of a standard guidance sampling.
I apologize if I misunderstood your code. Appreciate your reply.
Thanks for sharing the code. I have a question about the implementation of Sampson-guided sampling. As illustrated in Eq (8) in your paper, the probability of p(I|x_t) is a scaled gradient imposed on the noise predicted by the model, which is consistent with this paper. However, for corresponding implementation I found here: https://github.com/facebookresearch/PoseDiffusion/blob/36eeb1654ad8e67844672d7a40e7c179e9c58104/pose_diffusion/util/geometry_guided_sampling.py#L67-L126
https://github.com/facebookresearch/PoseDiffusion/blob/36eeb1654ad8e67844672d7a40e7c179e9c58104/pose_diffusion/util/geometry_guided_sampling.py#L129-L172
It seems that the model_mean is directly optimized by sampson-error rather than placed in the loop of a standard guidance sampling.
I apologize if I misunderstood your code. Appreciate your reply.