Multi-shot agentic video generation requires consistent character appearance, stable spatial layout across camera angles, and continuous character state between shots. When every shot is a separate request to a frozen generator, repeated text does not determine appearance, layout or state. We therefore recast the problem as condition construction and present MVAgent, a multi-agent pipeline whose agents collaborate through typed conditioning inputs. Because an environment image shows one viewpoint, a Spatial Grounding agent samples views from generated camera-traversal clips and anchors each shot to the view matching its framing. As generated shots drift from the plan, an Observer records how each shot ends in a continuity memory, from which a Transition agent builds character action and spatial references for the next shot. An Orchestrator composes these inputs into each request. Since a request reveals its effect only after rendering, we train it by agentic reinforcement learning with Trunk-GDPO, which compares rendered candidates at every shot rather than once per video and continues the best as the trunk. With generator and judges frozen, MVAgent attains the highest cross-shot consistency and narrative-planning quality among the compared methods on ViMax-Bench and is preferred over the strongest agentic baseline in human evaluation.
Figures & tables
Figure 1: MVAgent. (a) The Screenwriter and the Spatial Grounding agent supply the persistent assets, the environment image Ib and the multi-view library, which anchor character appearance and spatial layout. The Observer and the Transition agent supply the continuity memory and the reference I^t , which carry character state. The Orchestrator composes them into each request to the frozen video generator. (b) Trunk-GDPO trains the Orchestrator. A shot-level candidate group shares one state and policy version, all candidates are rendered and judged, components are normalised separately before aggregation, and the trunk advances the video, with asynchronous sampling and training.
Method
Medium
Long
Overall
CC
IC
GC
CC
IC
GC
CC
IC
GC
Veo 3.1
0.5536
0.5640
0.5582
0.4687
0.4778
0.4704
0.4978
0.5074
0.5005
IC-LoRA
0.5398
0.5713
0.5537
0.5015
0.5328
0.5087
0.5147
0.5460
0.5241
StoryDiffusion
0.5606
0.5796
0.5691
0.5007
0.5195
0.5048
0.5212
0.5401
0.5269
HoloCine
0.5366
0.5892
0.5597
0.4940
0.5565
0.5080
0.5098
0.5686
0.5272
ViMax (Gemini 3.1 Flash, Veo 3.1)
0.5681
0.5914
0.5785
0.5088
0.5402
0.5157
0.5291
0.5577
0.5372
Table 1: Cross-shot consistency on ViMax-Bench (ViCLIP cosine similarity for cross-scene CC, intra-scene IC and global GC, higher is better). Agentic rows name the LLM and the generator, bold and underline mark the highest and second-highest scores.
Figure 2: Qualitative comparison of three conditioning strategies for a multi-shot story. Each row shows keyframes generated with the same video model, story and camera specifications.
Backbone
CBC
NC
PPR
VSU
STQ
NF
Avg.
Ours-Gemini 3 Pro
4.37
4.08
4.19
4.83
4.01
2.64
4.02
Ours-Gemini 3.1 Flash
4.29
3.96
4.20
4.76
4.10
2.57
3.98
Ours-GPT-5.4
4.54
4.22
4.41
4.90
4.23
2.82
4.19
Table 2: Narrative-planning quality (1–5 scale) with different frozen agent backbones, following novel-to-video protocol of [ 6 ] .
MVAgent vs.
CSC
SF
AQ
Avg.
Veo 3.1
78.85%
78.38%
71.06%
76.07%
StoryDiffusion
82.79%
79.62%
77.94%
80.12%
HoloCine
65.31%
68.11%
57.45%
63.49%
ViMax
61.39%
63.02%
55.78%
60.06%
Table 3: Pairwise human preference rates (%) for MVAgent against each baseline on ViMax-Bench.
Variant
Consistency
Subjective evaluation (%)
CC
IC
GC
CSC
SF
AQ
Avg.
Portraits only
0.5286
0.5597
0.5360
71.5
56.3
53.9
60.6
w/o multi-view library
0.5319
0.5624
0.5388
69.1
55.8
52.7
59.2
Planned state only
0.5382
0.5690
0.5417
67.2
54.3
52.9
58.1
Ours (w/o RL)
0.5239
0.5496
0.5204
63.6
54.4
51.1
56.3
Episode-level RL
0.5362
0.5681
0.5403
55.0
52.9
51.2
53.0
Table 4: Ablation on ViMax-Bench with consistency as in Table 1 and preference rates (%) of the full model against each variant under the protocol of Table 3 .