CharacterShot: Controllable and Consistent 4D Character Animation
In this paper, we propose CharacterShot, a controllable and
consistent 4D character animation framework that enables any individual
designer to create dynamic 3D characters (i.e., 4D character animation) from a
single reference character image and a 2D pose sequence. We begin by
pretraining a powerful 2D character animation model based on a cutting-edge
DiT-based image-to-video model, which allows for any 2D pose sequnce as
controllable signal. We then lift the animation model from 2D to 3D through
introducing dual-attention module together with camera prior to generate
multi-view videos with spatial-temporal and spatial-view consistency. Finally,
we employ a novel neighbor-constrained 4D gaussian splatting optimization on
these multi-view videos, resulting in continuous and stable 4D character
representations. Moreover, to improve character-centric performance, we
construct a large-scale dataset Character4D, containing 13,115 unique
characters with diverse appearances and motions, rendered from multiple
viewpoints. Extensive experiments on our newly constructed benchmark,
CharacterBench, demonstrate that our approach outperforms current
state-of-the-art methods. Code, models, and datasets will be publicly available
at https://github.com/Jeoyal/CharacterShot.
689c1928fab6fdd2e52ac8a0
Junyao Gao
claimed_verified
2025-08-13T07:12:20.003Z
663b5f08389689b517109b77
/avatars/b8d66083b9e15c7f7924b4bbc8ca3861.svg
Gaojunyao
user
Gaojunyao
689c1928fab6fdd2e52ac8a1
Jiaxing Li
claimed_verified
2025-08-13T07:12:23.461Z
65464049e70ffa3c07f22e92
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/EAYwKThe-BGeza-CmzIdx.jpeg
 Li Jiaxing
user
LiJiaxing
689c1928fab6fdd2e52ac8a2
Wenran Liu
689c1928fab6fdd2e52ac8a3
Yanhong Zeng
689c1928fab6fdd2e52ac8a4
Fei Shen
689c1928fab6fdd2e52ac8a5
Kai Chen
689c1928fab6fdd2e52ac8a6
Yanan Sun
689c1928fab6fdd2e52ac8a7
Cairong Zhao
CharacterShot: Controllable and Consistent 4D Character Animation
In this paper, we propose CharacterShot, a controllable and
consistent 4D character animation framework that enables any individual
designer to create dynamic 3D characters (i.e., 4D character animation) from a
single reference character image and a 2D pose sequence. We begin by
pretraining a powerful 2D character animation model based on a cutting-edge
DiT-based image-to-video model, which allows for any 2D pose sequnce as
controllable signal. We then lift the animation model from 2D to 3D through
introducing dual-attention module together with camera prior to generate
multi-view videos with spatial-temporal and spatial-view consistency. Finally,
we employ a novel neighbor-constrained 4D gaussian splatting optimization on
these multi-view videos, resulting in continuous and stable 4D character
representations. Moreover, to improve character-centric performance, we
construct a large-scale dataset Character4D, containing 13,115 unique
characters with diverse appearances and motions, rendered from multiple
viewpoints. Extensive experiments on our newly constructed benchmark,
CharacterBench, demonstrate that our approach outperforms current
state-of-the-art methods. Code, models, and datasets will be publicly available
at https://github.com/Jeoyal/CharacterShot.
CharacterShot is a 4D character animation framework that uses a DiT-based model and dual-attention module to generate consistent 3D animations from a single image and 2D pose sequence.
DiT-based image-to-video model dual-attention module camera prior neighbor-constrained 4D gaussian splatting Character4D dataset CharacterBench benchmark