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D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

Reconstructing hand-held objects from a single RGB image is a challenging task in computer vision. In contrast to prior works that utilize deterministic modeling paradigms, we employ a point cloud denoising diffusion model to account for the probabilistic nature of this problem. In the core, we introduce centroid-fixed Dual-Stream Conditional diffusion for monocular hand-held object reconstruction (D-SCo), tackling two predominant challenges. First, to avoid the object centroid from deviating, we utilize a novel hand-constrained centroid fixing paradigm, enhancing the stability of diffusion and reverse processes and the precision of feature projection. Second, we introduce a dual-stream denoiser to semantically and geometrically model hand-object interactions with a novel unified hand-object semantic embedding, enhancing the reconstruction performance of the hand-occluded region of the object. Experiments on the synthetic ObMan dataset and three real-world datasets HO3D, MOW and DexYCB demonstrate that our approach can surpass all other state-of-the-art methods.

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References

  1. Boukhayma, A., Bem, R., Torr, P.H.: 3D hand shape and pose from images in the wild. In: CVPR, pp. 10843–10852 (2019)

    Google Scholar 

  2. Brahmbhatt, S., Tang, C., Twigg, C.D., Kemp, C.C., Hays, J.: ContactPose: a dataset of grasps with object contact and hand pose. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12358, pp. 361–378. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58601-0_22

    Chapter  Google Scholar 

  3. Calli, B., Singh, A., Walsman, A., Srinivasa, S., Abbeel, P., Dollar, A.M.: The YCB object and model set: towards common benchmarks for manipulation research. In: 2015 International Conference on Advanced Robotics (ICAR), pp. 510–517. IEEE (2015)

    Google Scholar 

  4. Cao, Z., Radosavovic, I., Kanazawa, A., Malik, J.: Reconstructing hand-object interactions in the wild. In: ICCV, pp. 12417–12426 (2021)

    Google Scholar 

  5. Chang, A.X., et al.: ShapeNet: an information-rich 3D model repository. arXiv preprint arXiv:1512.03012 (2015)

  6. Chao, Y.W., et al.: DexYCB: a benchmark for capturing hand grasping of objects. In: CVPR (2021)

    Google Scholar 

  7. Chen, Y., et al.: Joint hand-object 3d reconstruction from a single image with cross-branch feature fusion. IEEE TIP 30, 4008–4021 (2021)

    Google Scholar 

  8. Chen, Z., Chen, S., Schmid, C., Laptev, I.: gSDF: geometry-driven signed distance functions for 3D hand-object reconstruction. In: CVPR, pp. 12890–12900 (2023)

    Google Scholar 

  9. Chen, Z., Hasson, Y., Schmid, C., Laptev, I.: AlignSDF: pose-aligned signed distance fields for hand-object reconstruction. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision, ECCV 2022. LNCS, vol. 13661, pp. 231–248. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19769-7_14

  10. Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: CVPR, pp. 5939–5948 (2019)

    Google Scholar 

  11. Choe, J., Joung, B., Rameau, F., Park, J., Kweon, I.S.: Deep point cloud reconstruction. In: ICLR (2021)

    Google Scholar 

  12. Choy, C.B., Xu, D., Gwak, J.Y., Chen, K., Savarese, S.: 3D-R2N2: a unified approach for single and multi-view 3D object reconstruction. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 628–644. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_38

    Chapter  Google Scholar 

  13. Corona, E., Pumarola, A., Alenya, G., Moreno-Noguer, F., Rogez, G.: GanHand: predicting human grasp affordances in multi-object scenes. In: CVPR, pp. 5031–5041 (2020)

    Google Scholar 

  14. Damen, D., et al.: The epic-kitchens dataset: collection, challenges and baselines. IEEE TPAMI 43(11), 4125–4141 (2021). https://doi.org/10.1109/TPAMI.2020.2991965

    Article  Google Scholar 

  15. Damen, D., et al.: Scaling egocentric vision: the dataset. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 753–771. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_44

    Chapter  Google Scholar 

  16. Di, Y., et al.: CCD-3DR: consistent conditioning in diffusion for single-image 3D reconstruction. arXiv preprint arXiv:2308.07837 (2023)

  17. Doosti, B., Naha, S., Mirbagheri, M., Crandall, D.J.: HOPE-Net: a graph-based model for hand-object pose estimation. In: CVPR, pp. 6608–6617 (2020)

    Google Scholar 

  18. Dosovitskiy, A., et al.: An image is worth 16\(\times \)16 words: transformers for image recognition at scale. In: ICLR (2021)

    Google Scholar 

  19. Edelsbrunner, H., Kirkpatrick, D., Seidel, R.: On the shape of a set of points in the plane. IEEE Trans. Inf. Theor. 29(4), 551–559 (1983)

    Article  MathSciNet  Google Scholar 

  20. Edsinger, A., Kemp, C.C.: Human-robot interaction for cooperative manipulation: handing objects to one another. In: The 16th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2007, pp. 1167–1172. IEEE (2007)

    Google Scholar 

  21. Fan, H., Su, H., Guibas, L.J.: A point set generation network for 3D object reconstruction from a single image. In: CVPR, pp. 605–613 (2017)

    Google Scholar 

  22. Gao, H., Ji, S.: Graph U-Nets. In: ICML, pp. 2083–2092. PMLR (2019)

    Google Scholar 

  23. Gao, Q., Chen, Y., Ju, Z., Liang, Y.: Dynamic hand gesture recognition based on 3D hand pose estimation for human-robot interaction. IEEE Sens. J. 22(18), 17421–17430 (2021)

    Article  Google Scholar 

  24. Girdhar, R., Fouhey, D.F., Rodriguez, M., Gupta, A.: Learning a predictable and generative vector representation for objects. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 484–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_29

    Chapter  Google Scholar 

  25. Gkioxari, G., Malik, J., Johnson, J.: Mesh R-CNN. In: ICCV, pp. 9785–9795 (2019)

    Google Scholar 

  26. Grady, P., Tang, C., Twigg, C.D., Vo, M., Brahmbhatt, S., Kemp, C.C.: ContactOpt: optimizing contact to improve grasps. In: CVPR, pp. 1471–1481 (2021)

    Google Scholar 

  27. Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: A papier-mâché approach to learning 3D surface generation. In: CVPR, pp. 216–224 (2018)

    Google Scholar 

  28. Hampali, S., Rad, M., Oberweger, M., Lepetit, V.: HOnnotate: a method for 3D annotation of hand and object poses. In: CVPR, pp. 3196–3206 (2020)

    Google Scholar 

  29. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press (2003)

    Google Scholar 

  30. Hasson, Y., Tekin, B., Bogo, F., Laptev, I., Pollefeys, M., Schmid, C.: Leveraging photometric consistency over time for sparsely supervised hand-object reconstruction. In: CVPR, pp. 571–580 (2020)

    Google Scholar 

  31. Hasson, Y., et al.: Learning joint reconstruction of hands and manipulated objects. In: CVPR, pp. 11807–11816 (2019)

    Google Scholar 

  32. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)

    Google Scholar 

  33. Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: NeurIPS, vol. 33, pp. 6840–6851 (2020)

    Google Scholar 

  34. Iqbal, U., Molchanov, P., Breuel, T., Gall, J., Kautz, J.: Hand pose estimation via latent 2.5D heatmap regression. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 125–143. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6_8

    Chapter  Google Scholar 

  35. Kanazawa, A., Tulsiani, S., Efros, A.A., Malik, J.: Learning category-specific mesh reconstruction from image collections. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 386–402. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01267-0_23

    Chapter  Google Scholar 

  36. Kar, A., Tulsiani, S., Carreira, J., Malik, J.: Category-specific object reconstruction from a single image. In: CVPR, pp. 1966–1974 (2015)

    Google Scholar 

  37. Karunratanakul, K., Yang, J., Zhang, Y., Black, M.J., Muandet, K., Tang, S.: Grasping field: learning implicit representations for human grasps. In: 3DV, pp. 333–344. IEEE (2020)

    Google Scholar 

  38. Leng, Z., Chen, J., Shum, H.P., Li, F.W., Liang, X.: Stable hand pose estimation under tremor via graph neural network. In: 2021 IEEE Virtual Reality and 3D User Interfaces (VR), pp. 226–234. IEEE (2021)

    Google Scholar 

  39. Lin, C.H., Kong, C., Lucey, S.: Learning efficient point cloud generation for dense 3D object reconstruction. In: AAAI, vol. 32 (2018)

    Google Scholar 

  40. Liu, S., Jiang, H., Xu, J., Liu, S., Wang, X.: Semi-supervised 3D hand-object poses estimation with interactions in time. In: CVPR, pp. 14687–14697 (2021)

    Google Scholar 

  41. Liu, Z., Tang, H., Lin, Y., Han, S.: Point-voxel CNN for efficient 3D deep learning. In: NeurIPS, vol. 32 (2019)

    Google Scholar 

  42. Luo, S., Hu, W.: Diffusion probabilistic models for 3D point cloud generation. In: CVPR, pp. 2837–2845 (2021)

    Google Scholar 

  43. Luo, S., Hu, W.: Diffusion probabilistic models for 3D point cloud generation. In: CVPR, June 2021, pp. 2837–2845 (2021)

    Google Scholar 

  44. Melas-Kyriazi, L., Rupprecht, C., Vedaldi, A.: PC\(^2\): projection-conditioned point cloud diffusion for single-image 3D reconstruction. In: CVPR, June 2023, pp. 12923–12932 ()

    Google Scholar 

  45. Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 405–421. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_24

    Chapter  Google Scholar 

  46. Miller, A.T., Allen, P.K.: Graspit! a versatile simulator for robotic grasping. IEEE Robot. Autom. Mag. 11(4), 110–122 (2004)

    Article  Google Scholar 

  47. Mueller, F., et al.: GANerated hands for real-time 3D hand tracking from monocular RGB. In: CVPR, pp. 49–59 (2018)

    Google Scholar 

  48. Mueller, F., et al.: Real-time pose and shape reconstruction of two interacting hands with a single depth camera. ACM TOG 38(4), 1–13 (2019)

    Article  Google Scholar 

  49. Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: ICML, pp. 8162–8171. PMLR (2021)

    Google Scholar 

  50. Ortenzi, V., Cosgun, A., Pardi, T., Chan, W.P., Croft, E., Kulić, D.: Object handovers: a review for robotics. IEEE Trans. Rob. 37(6), 1855–1873 (2021)

    Article  Google Scholar 

  51. Panteleris, P., Oikonomidis, I., Argyros, A.: Using a single RGB frame for real time 3D hand pose estimation in the wild. In: WACV, pp. 436–445. IEEE (2018)

    Google Scholar 

  52. Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. In: CVPR, pp. 165–174 (2019)

    Google Scholar 

  53. Pham, T.H., Kyriazis, N., Argyros, A.A., Kheddar, A.: Hand-object contact force estimation from markerless visual tracking. IEEE TPAMI 40(12), 2883–2896 (2017)

    Article  Google Scholar 

  54. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: CVPR, pp. 652–660 (2017)

    Google Scholar 

  55. Qian, X., He, F., Hu, X., Wang, T., Ramani, K.: ARnnotate: an augmented reality interface for collecting custom dataset of 3D hand-object interaction pose estimation. In: Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, pp. 1–14 (2022)

    Google Scholar 

  56. Rantamaa, H.R., Kangas, J., Kumar, S.K., Mehtonen, H., Järnstedt, J., Raisamo, R.: Comparison of a VR stylus with a controller, hand tracking, and a mouse for object manipulation and medical marking tasks in virtual reality. Appl. Sci. 13(4), 2251 (2023)

    Article  Google Scholar 

  57. Ravi, N., et al.: Accelerating 3d deep learning with PyTorch3D. arXiv preprint arXiv:2007.08501 (2020)

  58. Rogez, G., Khademi, M., Supančič III, J.S., Montiel, J.M.M., Ramanan, D.: 3D hand pose detection in egocentric RGB-D images. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8925, pp. 356–371. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16178-5_25

    Chapter  Google Scholar 

  59. Rogez, G., Supancic, J.S., Ramanan, D.: Understanding everyday hands in action from RGB-D images. In: ICCV, pp. 3889–3897 (2015)

    Google Scholar 

  60. Romero, J., Tzionas, D., Black, M.J.: Embodied hands: modeling and capturing hands and bodies together. ACM TOG 36(6) (2017)

    Google Scholar 

  61. Rong, Y., Shiratori, T., Joo, H.: FrankMocap: a monocular 3D whole-body pose estimation system via regression and integration. In: ICCVW (2021)

    Google Scholar 

  62. Schönberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: CVPR (2016)

    Google Scholar 

  63. Schönberger, J.L., Zheng, E., Frahm, J.-M., Pollefeys, M.: Pixelwise view selection for unstructured multi-view stereo. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 501–518. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_31

    Chapter  Google Scholar 

  64. Seitz, S.M., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: CVPR, vol. 1, pp. 519–528. IEEE (2006)

    Google Scholar 

  65. Shan, D., Geng, J., Shu, M., Fouhey, D.F.: Understanding human hands in contact at internet scale. In: CVPR, pp. 9869–9878 (2020)

    Google Scholar 

  66. Sridhar, S., Mueller, F., Oulasvirta, A., Theobalt, C.: Fast and robust hand tracking using detection-guided optimization. In: CVPR, pp. 3213–3221 (2015)

    Google Scholar 

  67. Sridhar, S., Mueller, F., Zollhöfer, M., Casas, D., Oulasvirta, A., Theobalt, C.: Real-time joint tracking of a hand manipulating an object from RGB-D input. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 294–310. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_19

    Chapter  Google Scholar 

  68. Tatarchenko, M., Richter, S.R., Ranftl, R., Li, Z., Koltun, V., Brox, T.: What do single-view 3D reconstruction networks learn? In: CVPR, pp. 3405–3414 (2019)

    Google Scholar 

  69. Tekin, B., Bogo, F., Pollefeys, M.: H+O: unified egocentric recognition of 3D hand-object poses and interactions. In: CVPR, June 2019 (2019)

    Google Scholar 

  70. Tulsiani, S., Kar, A., Carreira, J., Malik, J.: Learning category-specific deformable 3D models for object reconstruction. IEEE TPAMI 39(4), 719–731 (2016)

    Article  Google Scholar 

  71. Tzionas, D., Ballan, L., Srikantha, A., Aponte, P., Pollefeys, M., Gall, J.: Capturing hands in action using discriminative salient points and physics simulation. IJCV 118, 172–193 (2016). https://doi.org/10.1007/s11263-016-0895-4

    Article  MathSciNet  Google Scholar 

  72. Tzionas, D., Gall, J.: 3D object reconstruction from hand-object interactions. In: ICCV, pp. 729–737 (2015)

    Google Scholar 

  73. Wu, J., Zhang, C., Xue, T., Freeman, B., Tenenbaum, J.: Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In: NeurIPS, vol. 29 (2016)

    Google Scholar 

  74. Xie, H., Yao, H., Sun, X., Zhou, S., Zhang, S.: Pix2Vox: context-aware 3D reconstruction from single and multi-view images. In: ICCV, pp. 2690–2698 (2019)

    Google Scholar 

  75. Xie, H., Yao, H., Zhang, S., Zhou, S., Sun, W.: Pix2Vox++: multi-scale context-aware 3D object reconstruction from single and multiple images. IJCV 128(12), 2919–2935 (2020). https://doi.org/10.1007/s11263-020-01347-6

    Article  Google Scholar 

  76. Yang, L., et al.: ArtiBoost: boosting articulated 3D hand-object pose estimation via online exploration and synthesis. In: CVPR, pp. 2750–2760 (2022)

    Google Scholar 

  77. Yang, L., Zhan, X., Li, K., Xu, W., Li, J., Lu, C.: CPF: learning a contact potential field to model the hand-object interaction. In: ICCV, pp. 11097–11106 (2021)

    Google Scholar 

  78. Ye, Y., Gupta, A., Tulsiani, S.: What’s in your hands? 3D reconstruction of generic objects in hands. In: CVPR, June 2022, pp. 3895–3905 (2022)

    Google Scholar 

  79. Zeng, X., et al.: LION: latent point diffusion models for 3D shape generation. In: NeurIPS (2022)

    Google Scholar 

  80. Zhang, C., et al.: DDF-HO: hand-held object reconstruction via conditional directed distance field. In: NeurIPS, vol. 36 (2024)

    Google Scholar 

  81. Zhang, C., et al.: MOHO: learning single-view hand-held object reconstruction with multi-view occlusion-aware supervision. arXiv preprint arXiv:2310.11696 (2024)

  82. Zhang, X., Li, Q., Mo, H., Zhang, W., Zheng, W.: End-to-end hand mesh recovery from a monocular RGB image. In: ICCV, pp. 2354–2364 (2019)

    Google Scholar 

  83. Zhou, L., Du, Y., Wu, J.: 3D shape generation and completion through point-voxel diffusion. In: ICCV, October 2021, pp. 5826–5835 (2021)

    Google Scholar 

  84. Zhou, Q.Y., Park, J., Koltun, V.: Open3D: a modern library for 3D data processing. arXiv preprint arXiv:1801.09847 (2018)

  85. Zhou, Y., Habermann, M., Xu, W., Habibie, I., Theobalt, C., Xu, F.: Monocular real-time hand shape and motion capture using multi-modal data. In: CVPR, pp. 5346–5355 (2020)

    Google Scholar 

  86. Zimmermann, C., Brox, T.: Learning to estimate 3d hand pose from single RGB images. In: ICCV, pp. 4903–4911 (2017)

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by the China Scholarship Council and in part by the Shuimu-Zhiyuan Tsinghua Scholar Program.

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Authors and Affiliations

  1. Tsinghua University, Beijing, China

    Bowen Fu, Gu Wang, Chenyangguang Zhang, Ziqin Huang & Xiangyang Ji

  2. Technical University of Munich, Munich, Germany

    Bowen Fu, Yan Di, Zhiying Leng & Federico Tombari

  3. Google, Zürich, Switzerland

    Fabian Manhardt & Federico Tombari

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  1. Bowen Fu
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Correspondence to Xiangyang Ji or Federico Tombari .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Fu, B. et al. (2025). D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15087. Springer, Cham. https://doi.org/10.1007/978-3-031-73397-0_22

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