thu-lyj-lab / t3bench Goto Github PK

Would it be possible to also release the human annotations? Thanks!

When using pip install for requirement.txt, salesforce-lavis, and image-reward are in conflict when sharing the transformer. They have different requirements for the version of the transformer.

Hello! Thanks for your interesting work. I have a question regarding the text prompts you release in this benchmark.
I am wondering if you are providing triplets of the increasingly descriptive text prompts for every object. or if every text prompt is referring to a random object.

For example:

• single object : "A red fire hydrant"
• single object with surroundings: "A red fire hydrant on a sidewalk"
• multiple objects: "A red fire hydrant is on a sidewalk close to a large tree".

Wonderful work! I've been following this work for a while and I noticed that in your latest submission for ECCV 24, several recent work like MVDream, DreamGaussian, etc. are added in the leaderboard. I'm wondering that whether the mesh results of these newly added methods can be released like the last update in 2023/10/24. I'm planning to leverage these mesh results as part of my dataset. I'll be very appreciated for their release. Overall, T3Bench is truly a contributing work and wish your submission for ECCV be accepted ASAP.
Thanks again! And look forward to your reply.

If I get an average alignment score 3,
then the normalized score is 3/5x100 = 60
or (3-1)/4x100 = 50 ?
Currently based on your paper sec 4.2, I assume the second calculation is correct.
Is that right?

Hello, thank you for providing a comprehensive benchmark for 3D generation. I am the author of GaussianDreamer and I would like to know how to submit our results on T3bench. Here are our results.

Hello, this is a wonderful job. I would like to know the generation time of these methods (ProlificDreamer, SJC, Fantasia3D, LatentNeRF, Magic3D, Dreamfusion) in the paper?