Error code:   ConfigNamesError
Exception:    BadZipFile
Message:      zipfiles that span multiple disks are not supported
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1031, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 996, in dataset_module_factory
                  return HubDatasetModuleFactory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 638, in get_module
                  module_name, default_builder_kwargs = infer_module_for_data_files(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 292, in infer_module_for_data_files
                  split_modules = {
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 293, in <dictcomp>
                  split: infer_module_for_data_files_list(data_files_list, download_config=download_config)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 234, in infer_module_for_data_files_list
                  return infer_module_for_data_files_list_in_archives(data_files_list, download_config=download_config)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 262, in infer_module_for_data_files_list_in_archives
                  for f in xglob(extracted, recursive=True, download_config=download_config)[
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 999, in xglob
                  fs, *_ = url_to_fs(urlpath, **storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 395, in url_to_fs
                  fs = filesystem(protocol, **inkwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 293, in filesystem
                  return cls(**storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 80, in __call__
                  obj = super().__call__(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 62, in __init__
                  self.zip = zipfile.ZipFile(
                File "/usr/local/lib/python3.9/zipfile.py", line 1266, in __init__
                  self._RealGetContents()
                File "/usr/local/lib/python3.9/zipfile.py", line 1329, in _RealGetContents
                  endrec = _EndRecData(fp)
                File "/usr/local/lib/python3.9/zipfile.py", line 286, in _EndRecData
                  return _EndRecData64(fpin, -sizeEndCentDir, endrec)
                File "/usr/local/lib/python3.9/zipfile.py", line 232, in _EndRecData64
                  raise BadZipFile("zipfiles that span multiple disks are not supported")
              zipfile.BadZipFile: zipfiles that span multiple disks are not supported

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Customized Human Object Interaction Image Generation

By Zhu Xu, Zhaowen Wang, Yuxin Peng, Yang Liu*

Accepted by ACM-MM 2025

Abstract

Compositional Customized Image Generation aims to customize multiple target concepts within generation content, which has gained attention for its wild application. Existing approaches mainly concentrate on the target entity's appearance preservation, while neglecting the fine-grained interaction control among target entities. To enable the model of such interaction control capability, we focus on human object interaction scenario and propose the task of Customized Human Object Interaction Image Generation(CHOI), which simultaneously requires identity preservation for target human object and the interaction semantic control between them. Two primary challenges exist for CHOI:(1)simultaneous identity preservation and interaction control demands require the model to decompose the human object into self-contained identity features and pose-oriented interaction features, while the current HOI image datasets fail to provide ideal samples for such feature-decomposed learning.(2)inappropriate spatial configuration between human and object may lead to the lack of desired interaction semantics. To tackle it, we first process a large-scale dataset, where each sample encompasses the same pair of human object involving different interactive poses. Then we design a two-stage model Interact-Custom, which firstly explicitly models the spatial configuration by generating a foreground mask depicting the interaction behavior, then under the guidance of this mask, we generate the target human object interacting while preserving their identities features. Furthermore, if the background image and the union location of where the target human object should appear are provided by users, Interact-Custom also provides the optional functionality to specify them, offering high content controllability. Extensive experiments on our tailored metrics for CHOI task demonstrate the effectiveness of our approach.

Dataset Structure

-data
   |--train
        |--image
        |--video
        |--video_2
   |--test
   |--annos
   |--hico_det_clip_instance

Bibtex

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