InternVL2_5-4B-Intrinsic-Judge

ReasonX — Intrinsic Judge

This is the MLLM judge released with ReasonX: MLLM-Guided Intrinsic Image Decomposition (CVPR 2026). It is fine-tuned from InternVL2.5-4B to make relative intrinsic comparisons between two marked points on an RGB image, across four physical modalities: depth, surface normals, albedo, and irradiance.

Paper: ReasonX: MLLM-Guided Intrinsic Image Decomposition
Project page: alaradirik.github.io/reasonx/
Code & Training Details: github.com/adobe-research/ReasonX

What does this model do?

Given an RGB image with two colored point markers (red and green), the judge answers modality-specific pairwise questions such as:

In the ReasonX framework, the frozen judge is used as a reward model inside a GRPO loop to fine-tune intrinsic decomposition models (PRISM, Marigold) on unlabeled, in-the-wild images without requiring ground-truth intrinsic maps. The judge achieves the following accuracy on held-out synthetic test sets (InteriorVerse / HyperSim):

Usage

The model expects an RGB image with two hollow circle markers drawn on it — one red (255, 0, 0) and one green (0, 255, 0) — indicating the two points to compare. Full point-drawing utilities and usage examples are available in the GitHub repository.

Quick start

import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained(
    "adirik/InternVL2_5-4B-Intrinsic-Judge",
    torch_dtype=torch.bfloat16,
    use_flash_attn=True,
    trust_remote_code=True,
).eval().cuda()

tokenizer = AutoTokenizer.from_pretrained(
    "adirik/InternVL2_5-4B-Intrinsic-Judge",
    trust_remote_code=True,
    use_fast=False,
)

# Load a point-pair annotated image (red and green markers drawn on RGB)
# See the GitHub repo for the load_image() and draw_points() utility functions
pixel_values = load_image("annotated_image.jpg").to(torch.bfloat16).cuda()

question = "Which point appears to be closer to the camera — red or green?"
generation_config = dict(max_new_tokens=128, do_sample=False)

response = model.chat(tokenizer, pixel_values, question, generation_config)
print(response)
# e.g. "The green point is closer."

Citation

If you use this model, please cite:

@inproceedings{Dirik2025ReasonXMI,
  title     = {ReasonX: MLLM-Guided Intrinsic Image Decomposition},
  author    = {Dirik, Alara and Wang, Tuanfeng and Ceylan, Duygu
               and Zafeiriou, Stefanos and Fr{\"u}hst{\"u}ck, Anna},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer
               Vision and Pattern Recognition (CVPR)},
  year      = {2026}
}