📷 CameraBench: Towards Understanding Camera Motions in Any Video

SfMs and VLMs performance on CameraBench: Generative VLMs (evaluated with VQAScore) trail classical SfM/SLAM in pure geometry, yet they outperform discriminative VLMs that rely on CLIPScore/ITMScore and—even better—capture scene‑aware semantic cues missed by SfM After simple supervised fine‑tuning (SFT) on ≈1,400 extra annotated clips, our 7B Qwen2.5‑VL doubles its AP, outperforming the current best MegaSAM!

📰 News

• [2025/04/26]🔥 We open‑sourced our fine‑tuned 7B model and the public test set—1 000+ videos with expert labels & captions. Stay tuned for stronger models in the future!
• LLMs‑eval integration is in progress—stay tuned!
• 32B & 72B checkpoints are on the way.

🌍 Explore More

• 🤗CameraBench Testset: Download the testset.
• 🚀Fine-tuned Model: Access model checkpoints!
• 🏠Home Page: Demos & docs.
• 📖Paper: Detailed information about CameraBench.
• 📈Leaderboard: Explore the full leaderboard..

🔎 VQA evaluation on VLMs

🤔: Does the camera track the subject from a side view? 🤖: ✅        🙋: ✅ 🤔: Does the camera only move down during the video? 🤖: ❌        🙋: ✅ 🤔: Does the camera move backward while zooming in? 🤖: ❌        🙋: ✅

🚀 Quick Start

Download test videos

python download_test_videos.py --save_dir ./your_target_folder

Get captions & labels (subset)

python download_test_data.py --save_dir ./your_target_folder

Use the finetuned model

We have released a preview version of our finetuned Qwen2.5-VL-7B model (which achieves SOTA performance on CameraBench!) on HuggingFace. The model is specialized for doing camerm motion primitive classification and video-text retrieval for camera-motion captions. The usage is identical to a Qwen2.5-VL model. A quick demo is shown below:

Generative Scoring (for classification and retrieval):

# Import necessary libraries
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch

# Load the model
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "chancharikm/qwen2.5-vl-7b-cam-motion-preview", torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")

# Prepare input data
video_path = "file:///path/to/video1.mp4"
text_description = "the camera tilting upward"
question = f"Does this video show \"{text_description}\"?"

# Format the input for the model
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "video",
                "video": video_path,
                "fps": 8.0,  # Recommended FPS for optimal inference
            },
            {"type": "text", "text": question},
        ],
    }
]

text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
    **video_kwargs
)
inputs = inputs.to("cuda")

# Generate with score output
with torch.inference_mode():
    outputs = model.generate(
        **inputs,
        max_new_tokens=1,
        do_sample=False,  # Use greedy decoding to get reliable logprobs
        output_scores=True,
        return_dict_in_generate=True
    )

# Calculate probability of "Yes" response
scores = outputs.scores[0]
probs = torch.nn.functional.softmax(scores, dim=-1)
yes_token_id = processor.tokenizer.encode("Yes")[0]
score = probs[0, yes_token_id].item()

print(f"Video: {video_path}")
print(f"Description: '{text_description}'")
print(f"Score: {score:.4f}")

Natural Language Generation

# The model is trained on 8.0 FPS which we recommend for optimal inference

from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info

# default: Load the model on the available device(s)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "chancharikm/qwen2.5-vl-7b-cam-motion-preview", torch_dtype="auto", device_map="auto"
)

# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
#     "chancharikm/qwen2.5-vl-7b-cam-motion-preview",
#     torch_dtype=torch.bfloat16,
#     attn_implementation="flash_attention_2",
#     device_map="auto",
# )

# default processor
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "video",
                "video": "file:///path/to/video1.mp4",
                "fps": 8.0,
            },
            {"type": "text", "text": "Describe the camera motion in this video."},
        ],
    }
]

text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    fps=fps,
    padding=True,
    return_tensors="pt",
    **video_kwargs,
)
inputs = inputs.to("cuda")

# Inference
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

✏️ Citation

If you find this repository useful for your research, please use the following.

@article{lin2025camerabench,
  title={Towards Understanding Camera Motions in Any Video},
  author={Lin, Zhiqiu and Cen, Siyuan and Jiang, Daniel and Karhade, Jay and Wang, Hewei and Mitra, Chancharik and Ling, Tiffany and Huang, Yuhan and Liu, Sifan and Chen, Mingyu and Zawar, Rushikesh and Bai, Xue and Du, Yilun and Gan, Chuang and Ramanan, Deva},
  journal={arXiv preprint arXiv:2504.15376},
  year={2025},
}