Open Infinity12306 opened 1 month ago
Hi!
Thanks for your interest in our work! We're working on releasing our code soon.
Embedding Non-Integer Values: We predefine some constants in Section 3.1 Quantization part. We multiply these non-integer values with the constants and take the integer part. We carefully select these constants (shown in the Implementation Details) to offer a good trade-off between maintaining the fidelity of sewing patterns and managing the vocabulary size.
Stitch Tag Calculation: Stitch tags are calculated based on Ground Truth garments. We follow a similar implement used in NeuralTailor.
Decoder Initialization: The decoder parameters are initialized with the pretrained LDM VAE and then fine-tuned. Our thinking is for diffuse maps, the encoder encodes the image to the latent code z, and the decoder decodes z to the original diffuse maps, which has the same function as the pretrained LDM VAE. However, for normal maps and roughness maps, we train the decoders that decode the latent code z (z is still from the diffuse map) to normal maps and roughness maps respectively. It is like we train two mappings for diffuse maps to normal maps and diffuse maps to roughness maps.
PBR Dataset Collection: The normal map and roughness map are paired with each diffuse map in the dataset, so we don't need to calculate them additionally.
Hello!
Congratulations on the acceptance of your paper; it's truly impressive work! I have a few questions regarding the detailed implementation and would greatly appreciate any insights from you or the community:
Thank you for your time and assistance!