We lifted 9 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| rehg-lab/rave | canonical | 8 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| HWC3 | Ran | rehg-lab/rave/annotator/util.py code served (permissive licence) · get_code("f9ec7d70add02b6f") |
| bilinear_sample | Ran | rehg-lab/rave/evaluation_uncleaned/eval_utils.py code served (permissive licence) · get_code("32613a47219e9a78") |
| make_layers | Ran | rehg-lab/rave/annotator/openpose/model.py code served (permissive licence) · get_code("0af8635db2bb32a2") |
| make_noise_disk | Ran | rehg-lab/rave/annotator/util.py code served (permissive licence) · get_code("e6559b118d17f6c8") |
| nms | Ran | rehg-lab/rave/annotator/util.py code served (permissive licence) · get_code("fa18e29c0ab322c6") |
| pad64 | Ran | rehg-lab/rave/evaluation_uncleaned/preprocesser_utils.py code served (permissive licence) · get_code("f526a743dde6eb58") |
| video_to_pil_list | Ran | rehg-lab/rave/evaluation_uncleaned/eval_utils.py code served (permissive licence) · get_code("5df4382c8e645d3a") |
| yaml_load | Ran | rehg-lab/rave/evaluation_uncleaned/preprocesser_utils.py code served (permissive licence) · get_code("194335e5792e5e74") |
| coords_grid | Not yet run | rehg-lab/rave/evaluation_uncleaned/eval_utils.py code served (permissive licence) · get_code("134980aa63271fbb") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recent advancements in diffusion-based models have demonstrated significant success in generating images from text. However, video editing models have not yet reached the same level of visual quality and user control. To address this, we introduce RAVE, a zero-shot video editing method that leverages pre-trained text-to-image diffusion models without additional training. RAVE takes an input video and a text prompt to produce high-quality videos while preserving the original motion and semantic structure. It employs a novel noise shuffling strategy, leveraging spatio-temporal interactions between frames, to produce temporally consistent videos faster than existing methods. It is also efficient in terms of memory requirements, allowing it to handle longer videos. RAVE is capable of a wide range of edits, from local attribute modifications to shape transformations. In order to demonstrate the versatility of RAVE, we create a comprehensive video evaluation dataset ranging from object-focused scenes to complex human activities like dancing and typing, and dynamic scenes featuring swimming fish and boats. Our qualitative and quantitative experiments highlight the effectiveness of RAVE in diverse video editing scenarios compared to existing methods. Our code, dataset and videos can be found in https://rave-video.github.io.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2312.04524")
get_code_for_paper("2312.04524")
have("2312.04524")
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