mix3r

Checkpoints

Due to limited data processing and training resources, we release two models (V1, V2). Link: https://modelscope.cn/models/jsnln00/mix3r/

Checkpoint Notes

• V1 is trained on ~400k trellis-generated data, with

  • no lighting effects;
  • center looking cameras;
  • batch size 16

• V2 is trained on ~200k blender-rendered data, with

  • PBR effects;
  • random lookat positions and roll angles;
  • batch size 8

V2 is generally recommended for in-the-wild images because it supports more flexible camera configurations

Pipelines

• For V1 use run_v1.py, which uses an additional recenter operation for stage-1 generation since V1 is trained with a center-looking camera configuration. However, this still may not work well with in-the-wild images.

• For V2 use run_v2.py, which does not recenter images.